40 Best IoT Project Ideas & Topics For Beginners 2024 [Latest]

Updated on 18 May, 2024

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Best IoT Project Ideas & Topics For Beginners

Table of Contents

  1. Why Build IoT-Based Projects?
  2. What are the benefits of IoT Projects Ideas for Final Year Students?
    1. 1. Improved Efficiency and Productivity
    2. 2. Enhanced Data Collection and Analysis
    3. 3. Cost Savings and Resource Management
    4. 4. Remote Monitoring and Control
    5. 5. Enhanced Customer Experience
    6. 6. Predictive Maintenance
    7. 7. Safety and Security
    8. 8. Sustainable and Eco-Friendly Solutions
    9. 9. Innovation and Competitiveness
    10. 10. Transforming Industries and Creating Smart Cities
  3. Top 24 Best IoT Projects Ideas For Final Year College Students & Beginners
    1. 1. Smart Agriculture System
    2. 2. Weather Reporting System
    3. 3. Home Automation System
    4. 4. Face Recognition Bot
    5. 5. Smart Garage Door
    6. 6. Smart Alarm Clock
    7. 7. Air Pollution Monitoring System
    8. 8. Smart Parking System
    9. 9. Smart Traffic Management System
    10. 10. Smart Cradle System
    11. 11. Smart Gas Leakage Detector Bot
    12. 12. Streetlight Monitoring System
    13. 13. Smart Anti-Theft System
    14. 14. Liquid Level Monitoring System
    15. 15. Night Patrol Robot
    16. 16. Health Monitoring System
    17. 17. Smart Irrigation System
    18. 18. Flood Detection System
    19. 19. Mining Worker Safety Helmet
    20. 20. Smart Energy Grid
    21. 21. Contactless Doorbell
    22. 22. Virtual Doctor Robot
    23. 23. Smart Waste Management System
    24. 24. Forest Fire Alarm System
    25. 25. Smart Baggage Tracker
    26. 26. Lavatory Vacant/Occupied System
    27. 27. Smart Pet Tracker
    28. 28. Plant Watering System
    29. 29. Home Energy Monitoring and Management
    30. 30. Health and Fitness Monitoring Device
    31. 31. Smart Pet Feeder
    32. 32. Water Quality Monitoring System
    33. 33. Safety Monitoring System for Manual Wheelchairs
    34. 34. Gesture-Controlled Contactless Switch for Smart Home
    35. 35. Automatic Emotion Journal
    36. 36. Cryptocurrency Alert System
    37. 37. Night Patrol Robot
    38. 38. Smart Banking System
    39. 39. Prison Break Monitoring And Alerting System
    40. 40. Customised Gaming Controller
  4. IoT Projects Examples
  5. Future for IoT
  6. What are some college projects for IoT?
  7. Wrapping Up
  8. Frequently Asked Questions (FAQs)

In this article, you will learn the 40Exciting IoT Project Ideas & Topics. Take a glimpse at the project ideas listed below.

Best Simple IoT Project Ideas & Topics

Here is the complete list of smart systems, formatted with numbers:

1. Smart Agriculture System
2. Weather Reporting System
3. Home Automation System
4. Face Recognition Bot
5. Smart Garage Door
6. Smart Alarm Clock
7. Air Pollution Monitoring System
8. Smart Parking System
9. Smart Traffic Management System
10. Smart Cradle System
11. Smart Gas Leakage Detector Bot
12. Streetlight Monitoring System
13. Smart Anti-Theft System
14. Liquid Level Monitoring System
15. Night Patrol Robot
16. Health Monitoring System
17. Smart Irrigation System
18. Flood Detection System
19. Mining Worker Safety Helmet
20. Smart Energy Grid
21. Contactless Doorbell
22. Virtual Doctor Robot
23. Smart Waste Management System
24. Forest Fire Alarm System
25. Smart Baggage Tracker
26. Lavatory Vacant/Occupied System
27. Smart Pet Tracker
28. Plant Watering System
29. Home Energy Monitoring and Management
30. Health and Fitness Monitoring Device
31. Smart Pet Feeder
32. Water Quality Monitoring System
33. Safety Monitoring System for Manual Wheelchairs
34. Gesture-Controlled Contactless Switch for Smart Home
35. Automatic Emotion Journal
36. Cryptocurrency Alert System
37. Night Patrol Robot
38. Smart Banking System
39. Prison Break Monitoring and Alerting System
40. Customised Gaming Controller

Read the full article to know more in detail.

IoT Project Ideas

We live in an exciting age of technological and digital revolution. In just a decade, we’ve witnessed a radical change in the world around us. Thanks to the recent advancements in Data Science, today, we have at our disposal things like AI-powered smart assistants, autonomous cars, surgical bots, intelligent cancer detection systems, and of course, the Internet of Things (IoT). So, if you are a beginner, the best thing you can do is work on some real-time IoT project ideas.

The world currently has around 15.14 billion IoT devices. And due to advancements in technologies like 5G, this number is projected to nearly double to 29.42 billion IoT devices by 2030. This indicates the IoT ecosystem is continuously expanding and evolving.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting IoT project ideas which beginners can work on to put their knowledge to test. In this article, you will find top IoT project ideas for beginners to get hands-on experience.

You can also check out our free courses offered by upGrad under machine learning and IT technology.

Why Build IoT-Based Projects?

But first, let’s address the more pertinent question that must be lurking in your mind: why build IoT projects?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience. The more you experiment with different IoT projects, the more knowledge you gain.

The Internet of Things is a major sensation of the 21st century. After all, who would have thought that someday we’d have access to a technology that would allow us to connect everyday objects – like thermostats, kitchen appliances, door lock systems, baby monitors, and electrical appliances – over a centralized and integrated network and control them from anywhere in the world!

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Essentially, IoT describes a connected network comprising multiple physical objects that have sensors and smart software embedded in them to facilitate the exchange of data among them via the Internet. However, IoT isn’t just limited to everyday household objects – you can even connect sophisticated industrial objects and systems over an IoT network. As of now, there are over 7 billion IoT devices, and this number is expected to grow to 22 billion by 2025!

An IoT network leverages a combination of mobile, cloud, and Big Data technologies along with data analytics and low-cost computing to enable the collection and exchange of data among physical objects connected within the network. And what’s impressive is that all of this is accomplished with minimal human intervention. 

As you start working on IoT project ideas, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career. Working on IoT simulation projects and IoT projects for engineering students is a fantastic way to improve efficiency and productivity. In this tutorial, you will find interesting IoT project ideas for beginners to get hands-on experience.

As the IoT technology continues to gain momentum in the modern industry, researchers and tech enthusiasts are readily investing in the development of pioneering IoT projects. In this post, we’ll talk about some of the best IoT project ideas.

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What are the benefits of IoT Projects Ideas for Final Year Students?

The Internet of Things (IoT) has emerged as a transformative force, connecting physical devices and everyday objects to the digital world. IoT projects encompass various applications across various sectors, from healthcare and agriculture to manufacturing and transportation. These IoT project ideas bring many benefits, revolutionizing industries and unprecedentedly enhancing lives.

1. Improved Efficiency and Productivity

One of the primary advantages of IoT projects is the ability to streamline processes and optimize resource usage. Businesses can monitor and manage operations in real time by deploying IoT-enabled sensors and devices. This leads to enhanced efficiency, reduced downtime, and improved overall productivity. For instance, in manufacturing, IoT sensors can track production lines, identifying bottlenecks and potential failures, allowing for timely maintenance and minimal disruptions.

2. Enhanced Data Collection and Analysis

IoT projects generate vast amounts of data from connected devices and sensors. This data offers valuable insights into operations, customer behavior, and equipment performance. Businesses can make informed decisions, identify trends, and predict outcomes through data analysis, leading to better planning and resource allocation.

3. Cost Savings and Resource Management

Optimizing resource usage not only improves efficiency but also leads to cost savings. IoT projects help organizations monitor energy consumption, water usage, and other resources, allowing for better control and conservation. Smart grids, for instance, can adjust energy distribution based on real-time demand, reducing waste and cutting costs for both providers and consumers.

4. Remote Monitoring and Control

IoT projects enable remote monitoring and control of devices and systems, offering convenience and safety. For example, IoT-enabled medical devices can transmit patient data to healthcare providers, enabling remote monitoring and timely intervention. Similarly, farmers can remotely monitor crops and irrigation systems in agriculture, optimizing agricultural practices and minimizing manual labor.

5. Enhanced Customer Experience

IoT applications can potentially revolutionize the customer experience by providing personalized and connected services. Smart homes with IoT devices offer seamless automation and control, enhancing comfort and convenience for residents. Retailers can leverage IoT data to offer personalized recommendations and targeted marketing, increasing customer satisfaction and loyalty.

Looking to challenge yourself or expand your portfolio? Check out our curated list of computer science project ideas to inspire your next groundbreaking project.

6. Predictive Maintenance

One of the most significant advantages of IoT projects is predictive maintenance. By continuously monitoring the condition of equipment and machinery, businesses can predict when maintenance is needed before a breakdown occurs. This approach reduces downtime, extends the lifespan of assets, and minimizes maintenance costs.

7. Safety and Security

IoT projects ideas can significantly improve safety in various environments. In industrial settings, IoT sensors can monitor workplace conditions, detect potential hazards, and ensure safety regulations compliance. Smart cities can use IoT to monitor traffic and public spaces, enhancing security and emergency response capabilities.

8. Sustainable and Eco-Friendly Solutions

IoT projects contribute to sustainability efforts by promoting smart and eco-friendly practices. Smart buildings can optimize energy consumption based on occupancy levels, reducing carbon footprints. IoT-enabled waste management systems can also improve recycling efforts and reduce waste generation.

9. Innovation and Competitiveness

Organizations that embrace IoT projects ideas gain a competitive edge by offering innovative solutions and services. IoT-driven insights and data analytics open new opportunities for businesses to differentiate themselves in the market and adapt to evolving customer needs.

10. Transforming Industries and Creating Smart Cities

They are instrumental in transforming industries and creating smart cities. IoT enables remote patient monitoring and telemedicine in healthcare, revolutionizing healthcare delivery. IoT-based precision farming techniques enhance crop yields while minimizing resource usage in agriculture. For transportation, IoT applications improve logistics and public transportation efficiency, reducing congestion and carbon emissions in smart cities.

So, here are a few IoT Project ideas that beginners can work on:

Top 24 Best IoT Projects Ideas For Final Year College Students & Beginners

This list of IoT project ideas for students is suited for beginners and those just starting out with IoT in general. These IoT project ideas will get you going with all the practicalities you need to succeed in your career. With a goal to keep up with advancing technologies, IoT projects for engineering students serve to be the blueprint to explore technological possibilities, a chance to produce, improve, and recreate technology capable of working on minimal human intervention. 

IoT research topics can help aspirants work on their practical skills and extend their subject knowledge further through consistent practice on IoT projects for engineering students. Further, this list should get you going if you’re looking for IoT project ideas for the final year. So, without further ado, let’s jump straight into some IoT project ideas that will strengthen your base and allow you to climb up the ladder.

1. Smart Agriculture System

One of the best ideas to start experimenting you hands-on IoT projects for students is working on a smart agriculture system. As the name suggests, this IoT-based project focuses on developing a smart agricultural system that can perform and even monitor a host of farming tasks. For instance, you can schedule the system to irrigate a piece of land automatically, or you can spray fertilizers/pesticides on the crops wirelessly through your smartphone.

Not just that, this IoT-based project can also successfully monitor soil moisture through a moisture sensing system, which can work to detect dry soil. Such an advanced system can handle routine agricultural tasks, thereby allowing farmers and cultivators to focus on more manual-intensive agricultural tasks. Learners can implement a similar IoT simulation project or IoT research topics to monitor house gardens or indoor plants that often go untended.

Benefits of smart agriculture system-

  • Real-time update
  • Increased productivity
  • Remote management
  • Timely monitoring
  • Data-centric
  • Lowered operation costs
  • Time effective
  • Accurate
  • Easy to use

Factors of smart agriculture-

  • Smart contracts
  • Supply Chain
  • Analytics
  • Soil factors
  • Climate
  • Sensors
  • Research
  • Storage

Source code – Github

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2. Weather Reporting System

This is one of the excellent IoT project ideas for beginners. This IoT-based weather reporting system is specifically designed to facilitate the reporting of weather parameters over the Internet. This is one of the best IoT projects where the system is embedded with temperature, humidity, and rain sensors that can monitor weather conditions and provide live reports of weather statistics. 

It is an always-on, automated system that sends data via a microcontroller to the web server using a WIFI connection. This data is updated live on the online server system. So, you can directly check the weather stats online without having to rely on the reports of weather forecasting agencies. The system also allows you to set threshold values and alerts for specific instances and notifies users every time the weather parameters cross the threshold value.

A few IoT projects for final year are aiming to evolve efficient usage of devices to reduce carbon footprint, which is a need of the hour. From consistent monitoring of carbon emissions to enforcing standard equipment and energy usage to operate under restricted levels, IoT’s role is evolving. Developers are leveraging smart technologies to maintain a consistent balance between nature and technology.

Benefits of Weather Reporting System-

  • Easy access to the weather report
  • Remote access
  • Compatible with various applications such as iOS, Android, etc.
  • Allows to take preventive measures
  • Allows the users to plan their activities
  • Can be carried anywhere
  • User friendly

Usage of Weather Reporting System-

  • Mountaineering
  • Agriculture
  • Fishing
  • Flood prediction
  • Defense
  • Aviation
  • Cyclone

Source code – Github

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3. Home Automation System

Home automation is perhaps the most talked about IoT projects. IoT-based home automation project aims to automate the functioning of household appliances and objects over the Internet. All the household objects that are connected over the IoT network can be controlled and operated through your smartphone.

This is not only convenient but also gives more power to the user to control and manage household appliances from any location in the world. 

This IoT-based project uses a touch-based home automation system. The components of this project include a WiFi connection, an AVR family microcontroller, and inbuilt touch-sensing input pins. While the microcontroller is integrated with the WiFi modem to obtain commands from the user via the Internet, an LCD screen displays the system status. When the microcontroller receives a command, it processes the instructions to operate the load accordingly and shows the system status on an LCD screen. 

However, also Blockchain IoT allows homeowners to manage their home security system remotely from their smartphone. Mentioning IoT projects can help your resume look much more interesting than others.

Benefits of Home Automation System-

  • Energy efficient
  • Safe and secure
  • Convenient
  • Time efficient
  • Remote access
  • Centralised managing point
  • Cost-effective
  • Constant monitoring 
  • Customisable according to the requirements

Usage of Home Automation System-

  • Electricity monitoring
  • Lawn management
  • The air quality of home
  • Home appliances of home
  • Smart assistants- Speech automated
  • Smart Locks
  • Smart Watches
  • Smart energy meters

Source code – Github

4. Face Recognition Bot

This IoT project involves building a smart AI bot equipped with advanced facial recognition capabilities. This is one of the best IoT Projects where the intelligent AI bot is designed to recognize the faces of different people or a single person and also their unique voice. 

The system includes facial recognition features like face detection (perceives faces and attributes the same in an image), personal identification (matches an individual in your private repository containing hundreds and thousands of people), and also emotion recognition (detects a range of facial expressions including happiness, contempt, neutrality, and fear).

This combination of advanced recognition features makes for a robust security system. The system also includes a camera that lets users preview live streams through face recognition.

Benefits of Face Recognition Bot-

  • Identification of missing individuals
  • Identification of criminals/ perpetrators
  • Protection from identity theft
  • Protection from business theft
  • Better photo organisation
  • Medical treatment

Significant aspects of facial recognition-

  • 3D mapping
  •  Biometric techniques
  • Deep learning
  • Face representation
  • Face detection
  • Face recognition

Source code – Github

5. Smart Garage Door

Yes, you can use IoT technology to control and operate your garage door! The IoT-based smart garage door eliminates the need for carrying bulky keychains. All you need is to configure and integrate your smartphone with the home IoT network, and you can effortlessly open or close your garage door with just a few clicks of a button.  

This smart garage door system incorporates laser and voice commands and smart notifications for monitoring purposes, and also IFTT integration that allows you to create custom commands for Google Assistant. The smart notification option can trigger alerts in real-time to notify as and when the garage door opens or closes, which is a nifty addition. This is one of the most straightforward IoT project ideas for you to work on.

Benefits of Smart Garage Door-

  • Secure
  • Safe
  • Remote access
  • Trackable
  • Time efficient
  • Protect deliveries
  • Schedule option 
  • Easy to install
  • User friendly
  • Can be accessed through various devices

Source code – Github

6. Smart Alarm Clock

This is one of the interesting IoT project ideas. This IoT-based alarm clock functions not only as an alarm clock to wake you up every morning, but it can convert into a fully-functional device capable of performing other tasks as well. The features of this smart alarm clock include:

  • Voice command option to execute standard commands and also to initiate a video chat.
  • A text-to-speech synthesizer
  • Automatic display brightness adjustment
  • Audio amplifier volume control 
  • Alphanumeric screen for displaying text

Apart from these features, you can also add customizable features to the smart alarm clock. Interestingly enough, the alarm clock offers three ways of waking you up – by playing local mp3 files, by playing tunes from the radio station, and by playing the latest news updates as podcasts.

Benefits of Smart Alarm Clock-

  • Helps in timeline management
  • Improves sleep quality
  • Increases productivity
  • It can be connected to various devices
  • Allows the users to integrate with the playlist

Components of Smart Alarm Clock-

  • Text-to-speech synthesiser
  • Keyboard
  • Display
  • Audio Amplifier
  • Button 
  • Speaker
  • Resistors 
  • Capacitors
  • Wires

Source code – Github

7. Air Pollution Monitoring System

One of the best ideas to start experimenting your hands-on IoT projects for students is working on an Air pollution monitoring system. Air pollution is a menace in all parts of the world, and monitoring air pollution levels is a challenge that we’re facing. While traditional air pollution monitoring systems fail to monitor air pollution levels successfully and the contaminants, IoT-based air pollution monitoring systems can both monitor the level of air pollution in cities and save the data on web servers for future use. 

This smart air pollution monitoring system promotes a cost-efficient technique for determining air quality. The system is embedded with sensors that specially monitor five components of the Environmental Protection Agency’s Air Quality Index – ozone, carbon monoxide, sulfur dioxide, nitrous oxide, and particulate matter. Plus, the system also includes a gas sensor that can alert users in case of gas leaks or the presence of flammable gases. Apart from this, there’s also a temperature and humidity sensor.

Benefits of Air Pollution Monitoring System-

  • It helps to monitor the pollutants
  • Allows the decision-makers to take preventive and corrective measures
  • Helps in improving the environment
  • Trackable
  • It helps to reduce the chances of health imbalance

Parameters to measure Air Pollution Monitoring System-

  • Wind speed
  • Rainfall
  • Radiation 
  • Temperature
  • Wind direction
  • Barometric pressure

Source code – Github

8. Smart Parking System

With cities and urban areas getting crowded by the minute, finding a parking space is nothing short of a challenge. It is not only time-consuming but also quite frustrating. Thanks to IoT, there’s a solution for solving the parking problem crisis. This IoT-based smart parking system is designed to avoid unnecessary traveling and harassment in the search for an appropriate parking area. This is an excellent IoT project for beginners.

So, if you are in a parking space, this system uses an IR sensor to monitor the entire area during the run time and provide you with an image for the same. This allows you to see any free spaces in the parking lot and drive straight to it without wasting any time looking for a parking space. Also, the system is tuned to open the car gate n only if there are empty slots available in a parking space.

  • Advanced Sensor Types: Utilizes ultrasonic, magnetic, or camera-based sensors to detect parking space availability.
  • Machine Learning Optimization: Employs machine learning algorithms to analyze and optimize parking space allocation.
  • Real-Time Data Analysis: Provides a central server that processes sensor data for real-time parking management.
  • Mobile App Integration: Offers a mobile app for drivers to access real-time updates on parking availability, location, and pricing.
  • Dynamic Pricing Models: Supports dynamic pricing based on parking demand to optimize revenue and usage.
  • Data Analytics for Urban Planning: Provides data analytics that can be used for broader urban planning and management.
  • Integration with City Traffic Systems: Integrates with city traffic systems to help reduce congestion and pollution.

Benefits of Smart Parking System-

  • Less fuel consumption
  • Time efficient
  • Cost efficient
  • Productivity
  • Optimised Parking
  • Real-time monitoring
  • Inclusive to disabled 
  • Parking guided systems
  • Online payments
  • The place to recharge electric vehicle
  • Space for special permits

Source code – Github

9. Smart Traffic Management System

As the population increases, the number of vehicles plying on the road also increases inevitably. Due to the ever-increasing number of both public and private cars in cities and metropolitan areas, traffic congestion has become an everyday problem. One of the needed and best IoT projects. To combat this problem, this IoT-based project creates a smart traffic management system that can effectively manage traffic on roads, and offer free pathways to emergency vehicles like ambulances and fire trucks. 

Emergency vehicles can connect to this smart system and find signals and pathways where the traffic flow can be controlled dynamically. It flashes a green notification light for emergency vehicles. Also, this intelligent traffic management system can identify and monitor traffic violators even at night.

Benefits of Smart Traffic Management System-

  • Real-Time Management of Traffic
  • Safety from road accidents
  • Preventive measures
  • Traffic monitoring
  • Better time management
  • Environmental impacts

Factors of Smart Traffic Management System-

  • Video Traffic Detection
  • Edge Processing Capabilities
  • Pollution Analytics
  • Predictive Planning
  • Shareable data

Source code – Github

10. Smart Cradle System

The whole concept behind creating the smart cradle is to enable parents to check up on their infants and monitor their activities from afar (remote locations). 

This is one of the interesting IoT project ideas. The IoT-based smart cradle system includes a cry-detecting mechanism and live-video surveillance along with a user interface (for mobile or web). The cradle is equipped with multiple sensors that can check and monitor the humidity and temperature of the bed. On the other hand, the surveillance camera attached to the cradle will continue to send footage of the infant to the parents.

The data generated by the sensors is stored in the cloud. Additionally, the system includes a health algorithm that feeds on the sensor data to continually check the health condition of the infant and alert the parents if it senses anything unusual in the baby’s health stats.

Benefits of Smart Cradle System-

  • Allows the parents to monitor their child.
  • Instant messages on ongoings.
  • Noise detection of the baby
  • Alerts on phone
  • Camera
  • Remote access
  • Shareable data

Features of a Smart Cradle System-

  • PIR sensor for child monitoring
  • Noise Detection
  • Camera
  • Swings on the cradle

Source code – Github

11. Smart Gas Leakage Detector Bot

Gas pipes are an indispensable component of both homes and industrial companies. Any leakage in gas pipes can lead to fire accidents and also contaminate the air with pollutants, thereby causing a disastrous effect on the air and the soil. This IoT-based project is explicitly built to combat the issue of gas leakage.

And this is the perfect idea for your next IoT project!

This tiny bot includes a gas sensor that can detect any gas leaks in a building. All you have to do is insert the bot into a pipe, and it will monitor the condition of the pipe as it moves forward. This is one of the most important and best IoT projects. In case the bot detects any gas leak in the pipeline, it will transmit the location of the leakage in the pipe via an interface GPS sensor over the IoT network. The bot uses IOTgecko to receive and display any gas leakage alert and its location over the IoT network. 

  • Multi-Gas Detection: Detects various gases like natural gas, propane, and carbon monoxide.
  • Integration with Smart Systems: Ability to integrate with smart home or industrial systems for enhanced safety protocols.
  • Automatic Safety Responses: Capable of triggering ventilation systems or shutting off gas supply lines automatically to mitigate risks.
  • Smartphone Notifications: Sends prompt notifications to smartphones or central monitoring systems when gas concentrations exceed safe levels.
  • Remote Monitoring and Control: Includes a Wi-Fi module for remote monitoring and control via smartphones.
  • Automatic Gas Valve Control: Features a motorized valve that can automatically turn off the gas valve to prevent further leakage.
  • Ease of Installation: Highlights the ease of installing the system for effective use.

Benefits of Smart Gas Leakage Detector Bot-

  • Early detection of toxic gases
  • Avoid unwanted leakages
  • Prevention from unwanted leakages

Features of Smart Gas Leakage Detector Bot-

  • LCD Display
  • IoT setup
  • Gas Sensor
  • Buzzer
  • Monitoring

Source code – Github

12. Streetlight Monitoring System

Streetlights are a significant source of energy consumption. Often, streetlights continue to remain on even when there’s no one in the street. With the help of this IoT-based streetlight monitoring system, we can efficiently monitor and optimize the energy consumption of streetlights.

In this IoT-based project, street lights are fitted with LDR sensors that can monitor the movement of humans or vehicles in the street. If the sensor can catch any movement in the street, it signals the microcontroller, which then turns on the street light. Similarly, if there’s movement in the street, the microcontroller switches the lights off. This way, a substantial amount of energy can be saved. This is one of the best IoT projects for safety. 

Not just that, the smart light system also allows users to monitor the estimated power consumption based on the current intensity of a streetlight. It is incorporated with a load-sensing functionality that can detect any fault in the lights. If the system detects an error, it automatically flags a particular light as faulty and sends the data over to the IoT monitoring system so that it can be fixed promptly.

Benefits of Streetlight Monitoring System-

  • Energy efficient
  • Cost-effective
  • Lower maintenance
  • Reduce carbon emissions
  • Improved infrastructure
  • Insights
  • Analysis

Features of Streetlight Monitoring System-

  • Digitally display signs
  • Detect weather conditions 
  • Monitor traffic 
  • Wifi hosting
  • Parking management
  • Alerts

Source code – Github

13. Smart Anti-Theft System

Security is one of the primary choices for homes, businesses, and corporations. Having a robust security system helps to keep unwanted intruders at bay. The IoT-based anti-theft system is the perfect solution for safeguarding homes as well as industrial enterprises. 

This IoT-based security system is programmed to monitor the entire floor of the building for tracking any kind of unusual movement. When turned on, a single movement could trigger an alarm, thereby alerting the owners of the property about unwanted visitors. It works something like this – whenever you vacate a house or a building, the Piezo sensor is turned on for tracking any movement in and around the property. This is one of the best IoT projects to practice. 

So if an intruder were to enter the property, the sensor would send the data to the microcontroller, which then converts it into a signal for the camera to snap a picture of the intruder. This picture is then automatically sent to the users on their smartphones. Mentioning IoT projects can help your resume look much more interesting than others.

Benefits of Smart Anti-Theft System-

  • Secure
  • Helps in the protection of belongings
  • Remote access
  • Integrates alert system
  • Allows the users to access it from any device
  • Alarm system

Factors of Smart Anti-Theft System-

  • Data capturing
  • Data storage
  • Data analysis
  • Shareable data
  • SMS option
  • Alert 
  • Door and Window Contacts
  • Motion Detectors
  • System Interruption Errors

Source code – Github

14. Liquid Level Monitoring System

This IoT-based project involves building a liquid-level monitoring system that can remotely monitor a particular liquid’s level and prevent it from overflowing. This project holds immense value for the industrial sector that uses large volumes of fluids in its day-to-day operations. Apart from detecting a liquid’s level, this monitoring system can also be used to track the usage of specific chemicals and to detect leaks in pipelines. 

The system is fitted with ultrasonic, conductive, and float sensors. A WiFi module helps connect the system to the Internet and facilitates data transmission. Four ultrasonic sensors help transmit the data on the liquid level and alert the user on the same. 

Benefits of Liquid Level Monitoring System-

  • Allows to access fluid level
  • Temperature monitoring
  • Updates 
  • Alarms
  • Automatic On/ OFF pumps
  • Level Control

Features of Liquid Level Monitoring System-

  • Remotely monitor liquid levels
  • Access fluid level information
  • Buzzer/ Trigger Alarms
  • Wi-Fi Modem 
  • Display levels of liquid

Source code – Github

15. Night Patrol Robot

This is one of the best IoT project ideas. It is a well-established fact that a majority of crimes occur in the dark, at night. This IoT project aims to develop a patrolling robot that can guard your home and property at night to prevent and reduce the possibilities of crimes. 

The patrol robot is equipped with a night vision camera with the help of which it can perform a 360-degree scan of a predefined path. It will scan a particular area, and if it detects human faces and movements, it will trigger an alarm to alert the user. The camera of the patrol robot can capture an intruder’s image and send the data to the user. The robot can function in a self-sufficient manner, without requiring you to hire security guards to protect your home.  

Benefits of Night Patrol Robot-

  • Secure
  • Increases safety
  • Helps in reducing the crime rates
  • Allows the government to track or trace criminals
  • Increases women’s safety
  • Strengthen surveillance efforts

Features of Night Patrol Robot-

  • Night vision
  • Motion Sensor
  • Display monitor
  • Wi-fi setup
  • Camera Capture
  • Speech recognition
  • Remote Access

Source code – Github

16. Health Monitoring System

This is one of the interesting IoT project ideas to create. This IoT-powered health monitoring system is designed to allow patients to take charge of their own health actively. The system will enable users to monitor their body vitals and send the data to qualified doctors and healthcare professionals. The doctors can then provide patients with immediate solutions and guidance based on their health condition. The sensors in the application can monitor patient vitals like blood pressure, sugar level, and heartbeat. If the vital stats are higher/lower than usual, the system will immediately alert the doctor. 

The idea behind creating this system is to allow patients and doctors to connect remotely for the exchange of medical data and expert supervision. You can use this application from any location in the world. It is an Arduino-based project – the communication occurs between the Arduino platform and an Android app via Bluetooth.

Benefits of Health Monitoring System

  • Cost-effective
  • Time effective
  • Accuracy
  • Easy access
  • Prompt diagnosis
  • Shareable
  • Health monitoring

Features of Health Monitoring System-

  • Sensor Module
  • Data Acquisition
  • Data Monitoring
  • Data Processing
  • Easy UI
  • Shareable
  • Wi-fi module

Source code – Github

17. Smart Irrigation System

Often, farmers have to irrigate the land manually. Not only is this a time-intensive task, but it is also labor-intensive. After all, it is quite challenging for farmers to continuously monitor the moisture level of the whole field and sprinkle the pieces of land that require water. This IoT project is a smart irrigation system that can analyze the moisture level of the soil and the climatic conditions and automatically water the field as and when required. 

You can use the smart irrigation system to check the moisture level, and set a predefined threshold for an optimum moisture level of soil, on reaching which the power supply will get cut off. An Arduino/328p microcontroller controls the motor that supplies water, and there’s an on/off switch with which you can start or stop the motor. The smart irrigation system will automatically stop if it starts raining.

Benefits of Smart Irrigation System-

  • Water conservation
  • Time efficient
  • Cost-effective
  • Remotely control sprinklers 
  • Increased soil quality
  • Sensors (Rain, Freeze, Wind, etc.)
  • Soil moisture sensor

Features of Smart Irrigation System-

  • Water Pump
  • Soil Moisture Sensor
  • Processing unit
  • Water Schedule Setup
  • Data Monitoring

Source code – Github

18. Flood Detection System

Floods are a common natural disaster that occurs almost every year in our country. Floods not only destroy agricultural fields and produce, but they also cause significant damage to vast stretches of area and property. This is why early flood detection is extremely vital to prevent the loss of life and valuable assets. 

This IoT-based flood detection system is built to monitor and track different natural factors (humidity, temperature, water level, etc.) to predict a flood, thereby allowing us to take the necessary measures to minimize the damage caused. This IoT project uses sensors to collect data for all the relevant natural factors. For instance, a digital temperature humidity sensor detects fluctuations in humidity and temperature. On the other hand, a float sensor continually monitors the water level. 

Besides providing a system equipped with temperature sensors and float sensors to gauge the possible flood conditions, comprehending the geographical features of the space can help create shelters and collect required amenities beforehand. At the same time, flood detection systems are capable enough to gauge the time a fresh wave of the flood could take to reach a particular location. Systems like these are significant to maintaining the well-being of communities. Advanced detection systems created through IoT projects for final year can alert residents in time, allowing for early evacuation planning.

Benefits of Flood Detection System

  • Risk Management
  • Helps in saving lives
  • Allows the stakeholders to save infrastructure
  • Cost-effective
  • Time effective
  • Real-time data
  • Flood forecasting
  • Mapping using GIS

Components of Flood Detection System-

  • Water Sensor
  • Wind Sensor
  • Data management
  • Ultrasonic sensor
  • Power Supply
  • Microcontrollers
  • Modem

Source code – Github

19. Mining Worker Safety Helmet

This is one of the interesting IoT project ideas. Mining workers work under extremely hazardous and dangerous conditions. Underground environments are full of risks, so there is always a fear of unpleasant accidents for miners. This mining worker safety helmet uses a microcontroller-based circuit to track the mining site’s environment and evaluate the safety of the workers. 

The safety helmet is equipped with an RF-based tracking system that helps transmit the data over the IoT network. An atmega microcontroller-based RF tracker circuit receives the data that is sent by the helmet nodes. Based on this data, the system maps the current location of workers in real time as they move through the mining site.

The helmet also includes a panic (emergency) button. If you press this button, an emergency sign will show up over the IoT web interface. This will alert the management to take the necessary steps for ensuring the workers’ safety.

Benefits of Mining Worker Safety Helmet-

  • Identification of the worker’s last location
  • Alarm in case of hazardous situation
  • Safety 
  • Safeguarding of lives
  • Infrastructure management
  • Time effective
  • Cost-effective

Features of Mining Worker Safety Helmet-

  • Cell place
  • Gas vent
  • Flexible button to untie
  • Sensors to send alarm 
  • Location tracker
  • Mini camera if required

Source code – Github

20. Smart Energy Grid

At present, energy grids are not optimized. Often when the electricity grid of a given region fails, the entire area suffers a blackout. This usually hinders the daily activities of people. This is one of the best IoT project ideas which proposes a solution to rectify this issue by creating a smart electricity grid.

This IoT-based smart energy grid uses an ATmega family controller to monitor and control the system’s activities. It uses WiFi technology to communicate over the Internet via the IoTGecko webpage. This smart grid’s primary task is to facilitate the transmission line’s re-connection to an active grid in case a particular grid fails.

So, if an energy grid becomes faulty, the system will switch to the transmission lines of another energy grid, thus, maintaining an uninterrupted electricity supply to the specific region whose energy grid failed. The system uses two bulbs to indicate valid and invalid users. Registered personnel can log in to the IoTGecko webpage and view updates on which grid is active and faulty. This is one of the best IoT Projects to add to your resume.

The smart energy grid can also monitor energy consumption and detect incidents of electricity theft.

Benefits of Smart Energy Grid-

  • Energy efficient
  • Resourceful
  • Time effective
  • Cost-effective
  • Improved reliability
  • Enhanced power quality
  • Reduce greenhouse gas emissions
  • Digitalisation
  • Decarbonisation

Source code – Github

21. Contactless Doorbell

All the systems around have become digitalised and automated. Covid on other hand has given a new perspective to contactless interaction.

The machine uses the raspberry pi controller. The machine also uses a camera and speaker for the process.

Benefits of Contactless  Doorbell-

  • Increased security
  • Prevention from thefts
  • Alert the owners
  • Voice assistance 
  • Alarm 
  • Wi-fi module
  • Camera capture
  • Remote access
  • Can be connected through various devices

Features of Contactless Doorbell-

  • Automatic visitor recognition
  • Power Supply
  • LAN/ Ethernet
  • Vision Sensor
  • PIR Sensor

Source code – Github

22. Virtual Doctor Robot

Doctors are highly required in the medical field. Their expertise saves lives every day, and they are seen as one of the most integral parts of our society. But with the rising cases and mishaps, especially in the case of emergencies and remote locations, it becomes difficult for doctors to be present everywhere. 

Virtual doctors play an important role to provide medical expertise even in remote locations. They could interact with the patients and provide medical advice just like a human. 

Benefits of Virtual Doctor Robot-

  • Inclusive to all types of locations
  • They could move around different locations
  • Assess medical reports over video call
  • Provide medical treatment at the earliest

Source code – Github

23. Smart Waste Management System

The cities are smarter and are keeping up with the technology. It is time to do away with the age-old practice of waste disposal and adapt to the smart waste management system.

Municipal professionals can make great use of this technology. Whenever the dustbin is about to be filled up totally, it sends an alarm or an alert to the team that they could fetch the waste in time. 

It also helps in segregating the waste into dry or wet garbage. Moreover, they could also help them to save energy and time.

Benefits of Smart Waste Management System-

  • Reduction of cost of collection
  • In time pickups
  • Stop overflowing of garbage
  • Environment friendly
  • CO2 Emission Reduction

Components of Smart Waste Management System-

  • IoT platform
  • Sensors
  • Integrated to various applications
  • Wi-fi 
  • Alarm/ Alert

Source code – Github

24. Forest Fire Alarm System

The machine helps to identify the causes of fire threats and take immediate measures to prevent those. This satellite and optical system can detect large landscapes. The alerts can be sent in time in order to take necessary actions in time. 

Benefits of Forest Fire Alarm System-

  • Safeguards environment
  • Helps to protect the environment, lives, infrastructure, and more.
  • Allows to gauge temperature, humidity, pressure, and wind
  • Geographical mapping of the location

Source code – Github

25. Smart Baggage Tracker

The Smart Baggage Tracker is one of the brilliant IoT project topics aimed at making traveling more convenient and stress-free. This project involves placing a small, lightweight device in your luggage that tracks its location in real-time. Using a smartphone app, you can quickly determine the exact whereabouts of your baggage at any time. 

Benefits of Smart Baggage Tracker-

  • Reduce the instances of lost or misplaced luggage.
  • Secured tagging in case of lost/stolen luggage. 
  • Weight monitoring 
  • Temperature control

Source code – Github

26. Lavatory Vacant/Occupied System

The Lavatory Vacant/Occupied System is a cutting-edge IoT project that offers a real-time solution for monitoring the occupancy of public and private restrooms. By using sensors and indicators, it provides instant updates on whether the restroom is available or in use. The system aims to optimize restroom management and enhance the overall user experience.

Benefits of Lavatory Vacant/Occupied System-

  • Improve privacy and user comfort.
  • Increase efficiency in cleaning and maintenance.
  • Reduce waiting times and manage queues effectively.
  • Enhance the utilization of facilities, particularly in high-traffic areas.
  • Foster sanitary conditions by preventing overcrowding.

Source code – Github

27. Smart Pet Tracker

The Smart Pet Tracker is a cutting-edge IoT-based project that aims to keep our beloved pets secure and healthy. Employing advanced tracking systems, this device can be easily attached to your pet’s collar, allowing you to monitor their whereabouts in real-time and guarantee their safety and well-being.

Benefits of the Smart Pet Tracker-

  • Know exactly where your pet is at any given time.
  • Get notified immediately if your pet leaves a pre-defined ‘safe zone’.
  • Understand your pet’s patterns and behaviors better.
  • Monitor your pet’s health and activity levels to ensure they’re staying active and healthy.
  • Adjust the tracking and alert parameters to suit your specific needs.
  • The Smart Pet Tracker is user-friendly and can be set up within minutes.
  • Affordable

Source code – Github

28. Plant Watering System

The IoT Plant Watering System is an interesting IoT-based mini project combining technology with nature to ensure plants are properly hydrated. This system uses sensors to check how moist the soil is and waters the plants automatically when the soil gets too dry. 

It’s a huge improvement for home gardeners because it reduces the chances of plants not getting enough or getting too much water.

Benefits include-

  • The system only waters plants when necessary, optimizing water usage.
  • The automated nature of the system relieves individuals from the need to manually water plants.
  • By maintaining appropriate moisture levels, the system promotes healthier and more productive plants.
  • This automation frees up time that can otherwise be used elsewhere.
  • The system can be scaled to suit everything from small household gardens to large agricultural fields.

Source code – Github

29. Home Energy Monitoring and Management

IoT has brought an exciting transformation in the Home Energy Monitoring and Management landscape. This great IoT project idea for beginners aims to provide homeowners with real-time data on their energy usage, allowing them to make informed decisions to minimize waste and reduce their energy bills.

Benefits-

  • Promotes conscious energy consumption, reducing waste, and promoting sustainability.
  • Homeowners can cut down their electricity bills by identifying and reducing unnecessary power usage.
  • The system is user-friendly, with a simple interface that doesn’t require technical expertise to operate.

Source code – Github

30. Health and Fitness Monitoring Device

The Health and Fitness Monitoring Device is an innovative solution that stands out among IoT projects. This device employs the principles of the Internet of Things (IoT) to monitor and track fitness metrics in real-time.

The benefits –

  • Allows integration of various technologies, offering students a practical understanding of IoT project ideas.
  • Facilitates real-time monitoring of health and fitness data, demonstrating the potential and utility of IoT in healthcare.

Source code – Github

31. Smart Pet Feeder

The IoT-based Smart Pet Feeder is an exciting and invaluable project idea for engineering students looking to delve into the world of IoT projects. This project is a perfect blend of technology and utility, designed to automatically feed pets at predetermined times.

The Smart Pet Feeder uses an IoT device to trigger the release of pet food from a dispenser into a feeding bowl. The owner can customize feeding schedules and portion sizes through a smartphone application, ensuring that pets follow a balanced diet even when the owner is not around.

Benefits of the Smart Pet Feeder-

  • Ensures that pets are fed at regular intervals without manual intervention.
  • Allows pet owners to customize feeding times and portion sizes based on their pets’ needs.
  • Offers owners the ability to monitor feeding schedules remotely, offering peace of mind.
  • Helps in maintaining a balanced diet for pets, contributing to their overall well-being.

Source code – Github

32. Water Quality Monitoring System

The Water Quality Monitoring System uses the IoT to monitor water quality in real-time. Such IoT-based projects are instrumental in addressing significant environmental issues, pushing them beyond the periphery of just IoT project ideas to something more impactful.

This project is incredibly important as it helps maintain the health and well-being of communities by ensuring clean and safe drinking water.

Benefits of the Water Quality Monitoring System-

  • Real-time monitoring and instant feedback on water quality.
  • Automation of the water monitoring process, reducing human error.
  • Potential for early detection of water contaminants, preventing health hazards.

Source code – Github

33. Safety Monitoring System for Manual Wheelchairs

The Safety Monitoring System for Manual Wheelchairs is a good example of IoT-based projects that offer life-improving solutions. This practical and vital IoT project idea uses a series of sensors and alarms to monitor the safety of wheelchair users.

Here are some key benefits-

  • Ensures user safety with real-time monitoring and hazard detection.
  • Increases user independence by enabling more confident navigation.
  • Provides peace of mind to caregivers with immediate alert systems.

Source code – Github

34. Gesture-Controlled Contactless Switch for Smart Home

The Gesture-Controlled Contactless Switch for Smart Homes is a cutting-edge IoT project for engineering students. This project uses gesture-recognition technology to operate switches without physical contact, contributing significantly to the development of smart homes.

Benefits of the Gesture-Controlled Contactless Switch for Smart Homes-

  • Enhance user convenience with easy and intuitive controls.
  • Improve safety by eliminating the need for physical contact with switches.
  • Facilitate energy efficiency through smart control of home appliances.

Source code – Github

35. Automatic Emotion Journal

The Automatic Emotion Journal is an excellent example that demonstrates how IoT can be integrated into our day-to-day lives, enhancing our emotional well-being.

This unique project uses IoT technology to capture an individual’s emotional state throughout the day. Using sensors and data analysis, it can record mood changes, providing a comprehensive emotional journal without the user having to manually input any information.

Benefits of the Automatic Emotion Journal-

  • Offers valuable insights into emotional patterns.
  • Encourages users to pay attention to their emotional well-being.

Source code – Github

36. Cryptocurrency Alert System

The Cryptocurrency Alert System is an innovative IoT project idea that brings together the worlds of technology and finance. This system monitors the volatile cryptocurrency market and sends real-time alerts based on specific conditions set by the user.

Benefits of the Cryptocurrency Alert System-

  • Empowers users with real-time data, enabling informed decision-making.
  • Encourages learning about both IoT and the burgeoning field of cryptocurrency.

Source code – Github

37. Night Patrol Robot

The Night Patrol Robot is an exceptional example of IoT projects that engineering students can undertake. Using advanced technology, this robotic device performs security patrols during nighttime hours, effectively providing a layer of safety and security wherever it’s deployed.

Source code – Github

38. Smart Banking System

The Smart Banking System is a prime example of IoT-based projects that students can take up to understand the practical applications of IoT. In this project, IoT technology is employed to enhance banking services like money transfer, making them more efficient and customer-friendly.

Benefits of the Smart Banking System-

  • Enhances understanding of how IoT can be employed to improve banking services continuously.

Source code – Github

39. Prison Break Monitoring And Alerting System

The Prison Break Monitoring and Alerting System is one of the innovative IoT projects that harness the power of IoT technology. This IoT project idea uses various sensors and alarms fused with IoT to monitor prison cells and alert the relevant authorities in case of any suspicious activities or breaches.

Key benefits-

  • Real-time monitoring provides comprehensive surveillance.
  • Automated alerts
  • Potentially save considerable amounts of money spent on traditional security measures.

Source code – Github

40. Customised Gaming Controller

The Customized Gaming Controller is an exciting IoT project that empowers students to design and build their own gaming gear.

Key benefits of Customised Gaming Controller-

  • Provides a hands-on approach to IoT concepts.
  • Students learn about circuitry, programming, and IoT technology, enhancing their tech-savvy skills.
  • The project encourages unique ideas and designs, fostering creativity amongst students.

Source code – Github

IoT Projects Examples

  • Smart Home Automation
  • Wearable Health Monitors
  • Smart Farming Systems
  • Industrial Internet of Things (IIoT)
  • Connected Car Applications
  • Smart Retail Systems
  • Energy Management Systems
  • Smart City Solutions
  • Environmental Monitoring
  • Smart Grid Technology

Future for IoT

With the ever-growing need for improvement and better accessibility, IoT estimates a dynamic future globally. Introduction to 5G and Metaverse are proof of the oncoming bright future for IoT’s flexible and improved variants. Assimilating the virtual world with reality through Metaverse is on its way, and IoT-based projects with source code are only a step away from joining hands to bring in digitally-driven physical devices. Cellular IoT’s growth is another aspect market expects to see in the coming years to adopt remote monitoring across diverse fields, including agriculture and smart cities. 

Extended IoT simulation projects are gaining popularity as a way to prepare young minds for the upcoming IoT trends. But perks are not the only thing accompanying IoT in the near future. 

Experts also predict heightened security threats for IoT-driven areas. A significant number of evolving IoT sectors are under the threat of botnets. In early 2021, sources reported a 35% to 51% spike in botnet attacks across individual devices and organizations through sophisticated instruments. As technological advancements improve, so do intrusion methods. Fortunately, constant improvements in security intelligence through IoT-based projects with source code are keeping such intrusions in check and aim to strengthen network and application firewalls further.

What are some college projects for IoT?

For college students exploring IoT, a variety of projects can provide practical experience and insight into this innovative field. Some potential project ideas include developing a Smart Irrigation System that uses sensors to optimize water usage in gardening, creating a Home Security System with motion detectors and remote alerts, or building an Energy Management System that monitors and controls energy consumption in real-time.

Other ideas could involve setting up an Environmental Monitoring System to track air quality or temperature changes, or implementing a Health Monitoring System that collects and analyzes data from wearable devices to track fitness and health metrics. These projects not only enhance technical skills but also encourage students to think creatively about solving real-world problems with technology.

Wrapping Up 

In this article, we have covered 24 IoT project ideas. These IoT-based projects are just a few examples of how IoT technology can be used and implemented to create innovative products. With further advancements in technology, it is highly likely that more such radical and groundbreaking IoT-based projects will enter the canvas of our everyday lives.

If you wish to improve your IoT skills, you need to get your hands on these IoT project ideasNow go ahead and put to test all the knowledge that you’ve gathered through our IoT project ideas guide to building your very own IoT Projects!

If you are interested to know more about IoT, deep learning, and artificial intelligence, check out our Executive PG Programme in Machine Learning & AI program which is designed for working professionals and provides 30+ case studies & assignments, 25+ industry mentorship sessions, 5+ practical hands-on capstone projects, more than 450 hours of rigorous training & job placement assistance with top firms.

upGrad partners with leading faculty and industry leaders to nurture dynamic young professionals and help them land lucrative jobs in the tech domain. Besides, learners get to have one-on-one sessions with professional mentors for extensive guidance and counseling.

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Frequently Asked Questions (FAQs)

1. How easy it is to implement these projects?

These projects are very basic, someone with a good knowledge of IoT can easily manage to pick and finish any of these projects.

2. Can I do this projects on Internship?

Yes, as mentioned, these project ideas are basically for Students or Beginners. There is a high possibility that you get to work on any of these project ideas during your internship.

3. Why do we need to build IoT projects?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience.

4. How is IoT useful in real life?

IoT is an integral part of our daily lives now; we all use IoT either knowingly or unknowingly. The best example of IoT in our day-to-day lives is home automation applications. Smart lights and smart blinds are becoming increasingly common today in modern smart homes. Then, our smartwatches that can track our heartbeat, count steps, etc., are also another brilliant application of IoT. Most of our smartphones come with biometric locks nowadays. These are again applications of IoT in real life. The barcode scanners we find in shopping malls are also IoT applications connected to computers and billing machines, which are all a part of the IoT network.

5. Do IoT engineers have to write code?

IoT devices or the hardware that we see are built up of several components, of which the IoT software needs to be programmed using computer languages. So IoT engineers have to write code using programming languages for IoT software to function. Several programming languages go into creating successful IoT applications, each with its own unique features and benefits. Some of the most commonly employed programming languages used are Python, Java, C++, MySQL, and C, among others. These programming languages are used to write the instructions contained in IoT software, which is embedded in the IoT hardware.

6. What skills and aptitude do you need to become an IoT developer?

If you aspire to become an IoT developer, then first and foremost, you need to have some basic familiarity with programming languages that are needed for IoT software development. Knowing Python and JavaScript can be an added advantage. Having an understanding of the role of data is vital in IoT. Trying your hands-on practice IoT projects is a brilliant way to gain confidence. Along with technical skills, soft skills are also indispensable in becoming a successful IoT developer.

Did you find this article helpful?

Kechit Goyal

Experienced Developer, Team Player and a Leader with a demonstrated history of working in startups. Strong engineering professional with a Bachelor of Technology (BTech) focused in Computer Science from Indian Institute of Technology, Delhi.

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Here are a few examples of Machine Learning applications you might be familiar with: Online recommendations from Amazon and Netflix. YouTube detecting and removing terror content on the platform. Knowing what customers are saying about you on Twitter The Rise of Machine Learning The emergence of the internet, as well as the massive increase in digital information being generated, stored, and made available for analysis, are seen to be the two important factors that have led to the emergence of Machine Learning. With the magnitude of quality data from the internet, economical data storage options and improved data processing capabilities, Machine Learning algorithms are seen as a vehicle propelling the development of Artificial Intelligence at a scorching pace in recent times. Neural Networks A neural network works on a system of probability by being able to make statements, decisions, or predictions based on data fed to it. Moreover, a feedback loop enables further “learning” by sensing; it also modifies the learning process based on whether its decisions are right or wrong. An artificial neural network is a computer system with node networks inspired from the neurons in the animal brain. Such networks can be taught to recognise and classify patterns through witnessing examples rather than telling the algorithm how exactly to recognise and classify patterns. Machine Learning derived applications of neural networks can read pieces of text and recognise the nature of the text – whether it is a complaint or congratulatory note. They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of similar music. What’s more, they can even compose music expressing the same mood or theme. In the near future, with the help of Machine Learning and Artificial Intelligence, it should be possible for a person to communicate and interact with electronic devices and digital information thanks to another emerging field of AI called Natural Language Processing (NLP). NLP has become a source of cutting-edge innovation in the past few years, and one which is heavily reliant on Machine Learning. NLP applications attempt to understand human communication, both written as well as spoken, and communicate using various languages. In this context, Machine Learning helps machines understand the nuances in human language and respond in a way that a particular audience is likely to comprehend. So, who is actually using it? Most industries working with large amounts of data have recognised the value of Machine Learning. Large companies glean vital real-time actionable insights from stored data and are hence able to increase efficiency or gain an advantage over their competitors. Financial services Banks and other businesses use Machine Learning to identify important insights in data generated and thereby prevent frauds. These insights can identify investment opportunities or help investors know when to trade. Data mining can also identify clients with high-risk profiles or use cyber surveillance to warn customers about fraud and thereby minimise identity theft. Marketing and sales E-commerce websites use Machine Learning technology to analyse buying history based on previous purchases, to recommend items that you may like and promote other items. The retail industry is enlisting the ability of websites to capture data, analyse it, and use it to personalise a shopping experience or implement marketing campaigns. Summing up, Artificial Intelligence and, in particular, Machine Learning, certainly has a lot to offer today. With its promise of automating mundane tasks as well as offering creative insights, industries in every sector from banking to healthcare and manufacturing are reaping the benefits. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Eventually, scientists hope to develop human-like Artificial Intelligence that is capable of increasing the speed of various automated functions, especially with the advent of chatbots in the internet realm. Much of the exciting progress that we have seen in recent years is due to progressive changes in Artificial Intelligence, which have been brought about by Machine Learning. This is clearly why Machine Learning is poised to become the next big thing in the data sciences sphere. So go ahead, UpGrad yourself to stay ahead of the curve.
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by Varun Dattaraj

17 Oct'17
The Difference between Data Science, Machine Learning and Big Data!

7.86K+

The Difference between Data Science, Machine Learning and Big Data!

Many professionals and ‘Data’ enthusiasts often ask, “What’s the difference between Data Science, Machine Learning and Big Data?” This is a question frequently asked nowadays. Here’s what differentiates Data Science, Machine Learning and Big Data from each other: Data Science Data Science follows an interdisciplinary approach. It lies at the intersection of Maths, Statistics, Artificial Intelligence, Software Engineering and Design Thinking. Data Science deals with data collection, cleaning, analysis, visualisation, model creation, model validation, prediction, designing experiments, hypothesis testing and much more. The aim of all these steps is just to derive insights from data. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification Digitisation is progressing at an exponential rate. Internet accessibility is improving at breakneck speed. More and more people are getting absorbed into the digital ecosystem. All these activities are generating a humongous amount of data. Companies are currently sitting on a data landmine. But data, by itself, is not of much use. This is where Data Science comes into the picture. It helps in mining this data and deriving insights from it; for taking meaningful action. Various Data Science tools can help us in the process of insight generation. If you are a beginner and interested to learn more about data science, check out our data scientist courses from top universities. Frameworks exist to help derive insights from data. A framework is nothing but a supportive structure. It’s a lifecycle used to structure the development of Data Science projects. A lifecycle outlines the steps —  from start to finish — that projects usually follow. In other words, it breaks down the complex challenges into simple steps. This ensures that any significant phase, which leads to the generation of actionable insights from data, is not missed out. One such framework is the ‘Cross Industry Standard Process for Data Mining’, abbreviated as the CRISP-DM framework. The other is the ‘Team Data Science Process’ (TDSP) from Microsoft. Let’s understand this with the help of an example. A bank named ‘X’, which has been in business for the past ten years. It receives a loan application from one of its customers. Now, it wants to predict whether this customer will default in repaying the loan. How can the bank go about achieving this task? Like every other bank, X must have captured data regarding various aspects of their customers, such as demographic data, customer-related data, etc. In the past ten years, many customers would have succeeded in repaying the loan, but some customers would have defaulted. How can this bank leverage this data to improve its profitability? To put it simply, how can it avoid providing loans to a customer who is very likely to default? How can they ensure not losing out on good customers who are more likely to repay their debts? Data Science can help us resolve this challenge. Raw Data —> Data Science —-> Actionable Insights Let’s understand how various branches of Data Science will help the bank overcome its challenge. Statistics will assist in the designing of experiments, finding a correlation between variables, hypothesis testing, exploratory data analysis, etc. In this case, the loan purpose or educational qualifications of the customer could influence their loan default. After performing data cleaning and exploratory study, the data becomes ready for modeling. Statistics and artificial intelligence provide algorithms for model creation. Model creation is where machine learning comes into the picture. Machine learning is a branch of artificial intelligence that is utilised by data science to achieve its objectives. Before proceeding with the banking example, let’s understand what machine learning is. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Enrol for the Machine Learning Course from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Machine Learning “Machine learning is a form of artificial intelligence. It gives machines the ability to learn, without being explicitly programmed.” How can machines learn without being explicitly programmed, you might ask? Aren’t computers just devices made to follow instructions? Not anymore. Machine learning consists of a suite of intelligent algorithms, enabling machines to learn without being explicitly programmed for it. Machine learning helps you learn the objective function — which maps the inputs to the target variable, or independent variables to the dependent variables. In our banking example, the objective function determines the various demographics, customer and behavioural variables which influences the probability of a loan default. Independent attributes or inputs are the demographic, customer and behavioural variables of a customer. The dependent variable is either ‘to default’ or not. The objective function is an equation which maps these inputs to outputs. It’s a function which tells us which independent variables influence the dependent variable, i.e. the tendency to default. This process of deriving an objective function, which maps inputs to outputs is known as modelling. Initially, this objective function will not be able to predict precisely whether a customer will default or not. As the model encounters new instances, it learns and evolves. It improves as more and more examples become available. Ultimately, this model reaches a stage where it will be able to tell with a certain degree of precision. hings like, which customer is going to default, and whom the bank can rely on to improve its profitability. Machine learning aims to achieve ‘generalisability’. This means, the objective function — which maps the inputs to the output — should apply to the data, which hasn’t encountered it, yet. In the banking example, our model learns patterns from the data provided to it. The model determines which variables will influence the tendency to default. If a new customer applies for a loan, at this point, his/her variables are not yet seen by this model. The model should be relevant to this customer as well. It should predict reliably whether this customer will default or not. If this model is unable to do this, then it will not able to generalise the unseen data. It is an iterative process. We need to create many models to see which work, and which don’t. Data science and analysis utilise machine learning for this kind of model creation and validation. It is important to note that all the algorithms for this model creation do not come from machine learning. They can enter from various other fields. The model needs to be kept relevant at all times. If the conditions change, then the model — which we created earlier — may become irrelevant. The model needs to be checked for its predictability at different times and needs to be modified if its predictability reduces. For the banking employee to take an instant decision the moment a customer applies for a loan, the model needs to be integrated with the bank’s IT systems. The bank’s servers should host the model. As a customer applies for a loan, his variables must be captured from a website and utilised by the model running on the server. Then, this model should convey the decision — whether the credit can be granted or not — to the bank employee, instantly. This process comes under the domain of information technology, which is also utilised by data science. In the end, it is all about communicating the results from the analysis. Here, the presentation and storytelling skills are required to demonstrate the effects from the study efficiently. Design-thinking helps in visualising the results, and effectively tell the story from the analysis. Big Data The final piece of our puzzle is ‘Big Data’. How is it different from data science and machine learning? According to IBM, we create 2.5 Quintillion (2.5 × 1018) bytes of data every day! The amount of data which companies gather is so vast that it creates a large set of challenges regarding data acquisition, storage, analysis and visualisation. The problem is not entirely regarding the quantity of data that is available, but also its variety, veracity and velocity. All these challenges necessitated a new set of methods and techniques to deal with the same. Big data involves the four ‘V’s — Volume, Variety, Veracity, and Velocity — which differentiates it from conventional data. Volume: The amount of data involved here is so humongous, that it requires specialised infrastructure to acquire, store and analyse it. Distributed and parallel computing methods are employed to handle this volume of data. Variety: Data comes in various formats; structured or unstructured, etc. Structured means neatly arranged rows and columns. Unstructured means that it comes in the form of paragraphs, videos and images, etc. This kind of data also consists of a lot of information. Unstructured data requires different database systems than traditional RDBMS. Cassandra is one such database to manage unstructured data. Veracity:  The presence of huge volumes of data will not lead to actionable insights. It needs to be correct for it to be meaningful. Extreme care needs to be taken to make sure that the data captured is accurate, and that the sanctity is maintained, as it increases in volume and variety. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Velocity: It refers to the speed at which the data is generated. 90% of data in today’s world was created in the last two years alone. However, this velocity of information generated is bringing its own set of challenges. For some businesses, real-time analysis is crucial. Any delay will reduce the value of the data and its analysis for business. Spark is one such platform which helps analyse streaming data. As time progresses, new ‘V’s get added to the definition of big data. But — volume, variety, veracity, and velocity — are the four essential constituents which differentiate data from big data. The algorithms which deal with big data, including machine learning algorithms, are optimised to leverage a different hardware infrastructure, that is utilised to handle big data. To summarise, Executive PG Programme in Data Science is an interdisciplinary field with an aim to derive actionable insights from data. Machine learning is a branch of artificial intelligence which is utilised by data science to teach the machines the ability to learn, without being explicitly programmed. Volume, variety, veracity, and velocity are the four important constituents which differentiate big data from conventional data.
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Natural Language Generation: Top Things You Need to Know

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Natural Language Generation: Top Things You Need to Know

From a linguistic point of view, language was created for the survival of human beings. The effective communication helped a primitive man to hunt, gather and survive in groups. This means a language is necessary to carry out all activities needed for not only survival but also a meaningful existence of human beings. As humans evolved so did their literary skills. From pictorial scripts to well developed universal ones, we have made an impressive progress. In fact, such remarkable progress that a machine developed by humans now can read data, write text and not in a machine, binary language but a real, conversational language. Natural Language Generation has made this possible. Top Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our certification courses on AI & ML, kindly visit our page below. Machine Learning Certification What is Natural Language Generation? Natural language is an offshoot of Artificial Intelligence. It is a tool to automatically analyse data, interpret it, identify the important information and narrow it down to a simple text, to make decision making in business easier, faster and of course, cheaper. It crunches numbers and drafts a narrative for you. Trending Machine Learning Skills AI Courses Tableau Certification Natural Language Processing Deep Learning AI Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. What are the different variations of Natural Language Generation? Basic Natural Language Generation: The basic form of NLG converts data into text through Excel-like functions. For example, a mail merge that restates numbers into a language. Templated Natural Language Generation: In this type of NGL tool, a user takes the call on designing content templates and interpreting the output. Templated systems are restricted in their capability to scan multiple data sources, perform advanced analytics. Advanced Natural Language Generation: It is the ‘smartest’ way of analysing data. It processes the data right from the beginning and separates it based on its significance for a particular audience, and then writes the narrative with relevant information in a conversational tone. For example, if a data analyst wants to know how a particular product is doing in a market, an advanced NLG tool would write a report by segregating the data of only the required product. Do we really need natural language generation? A number of devices are connected to the internet creating a huge Internet of Things. All these devices are creating data at a lightning speed leading to Big Data generation. It is almost humanly impossible to analyse, interpret and draw rational interference from this enormous data. Along with data analysis and accurate interpretation the need for the optimum use of resources, cost cutting and time management are the essentials for a modern business to survive, grow and flourish. Natural Language Generation helps up to effectively achieve all these goals in one go. Additionally, when a machine can do these routine tasks, and accurately. So, valuable human resources can indulge themselves in the activities that require innovation, creativity and problem-solving. Will Natural Language Generation kill jobs? First of all, not all kinds of narratives can be written by Natural Language Generation tools. It is only for creating a text based on data. Creative writing, engaging content is developed not only by analytical skills but with the help of major emotional involvement. The passion of an individual, their skills, their ability to cater complex terms in simpler formats can’t be replaced. Additionally, to rationalise the text created by Natural Language Generation tools, human intervention is critical. Natural Language Generation only augments the job and enriches the life of employees by freeing them from menial jobs. Alain Kaeser, founder of Yseop has rightly acknowledged that- “The next industrial revolution will be the artificial intelligence revolution and the automation of knowledge work and repetitive tasks to enhance human capacity”. Why should you get a hang of Natural Language Generation? A research commissioned by Forrester Research anticipated a 300% increase in investment in artificial intelligence in 2017 compared to 2016. The Artificial Intelligence market will grow from $8 billion in 2016 to more than $47 billion in 2020. Based on this report, Forbes magazine has come up with a list of the ‘hottest ten Artificial Intelligence technologies’ that will rule the market in the near future. Natural Language Generation is one of them and it is set to see a huge boost. Examples and Applications of Natural Language Generation Natural Language Generation techniques are put to use across various industries as per their requirements. Healthcare-Pharma, Banking services, Digital marketing… it’s everywhere! From fund reporting in finance and campaign analytics reporting in marketing to personalised client alerts for preparing dashboards in sales and customer service maintenance, it is used to generate effective results for all departments in an organisation. Let’s have a quick look at how NLG has varied applications in various departments: Marketing – Two main responsibilities of a marketing department are designing market strategy and conducting market research. Both of these activities heavily depend on data analysis, and in today’s world of big data, it is becoming increasingly complex. Natural Language Generation tools can help you scan big data, analyse it and write reports for you within a few hours. Sales – A sales analysis report indicates the trends in a company’s sales volume over a period of time. A sales analysis report throws light on the factors that affects sales, like season, competitors strategy, advertising efforts etc. Managers use sales analysis reports to recognise market opportunities and areas where they could increase volume. These reports are purely based on humongous data. Natural Language Generation programs save your time and efforts of manually scanning data, finding trends and writing reports. Once you feed the inputs, it takes care of all of these activities. Banking and finance – May it be a finance department of an organisation or an investment bank, financial reports stating the financial health of a company needs to be written and sent out to shareholders, investors, rating agencies, government agencies etc. The general financial statements like balance sheets, Statement of cash flows, Income statement etc. are loaded with numbers and a reader likes to have a quick understanding of these statements. Natural Language Generation software scans through these statements and presents this information in a simple, text format rather than complicated accounting one. Healthcare and medicine – Recently Natural Language Generation tools are being used to summarise e-medical records. Additional research in this area is opening doors to prudent medical decision-making for medical professionals. It is also being used in communicating with patients, as a part of patient awareness programs in India, as per the NCBI report. The data collected through medical research like what kind of lifestyle diseases are most dreadful or what kinds of habits are healthy can be summarized in a simple language for patients which is extremely useful for the doctors to make a case for their advice. And this is just the tip of the iceberg. The applications of NLG tools are widespread already and are ready to take off to greater heights in the future.   Techniques of natural language generation – How to get started A refined Natural Language Generation system needs to inject some aspects of planning and amalgamation of information to enable the NLG tools to generate the text which appears natural and interesting. The general stages of natural language generation, as proposed by Dale and Reiter in their book ‘Building Natural Language Generation Systems’ are: Content determination: In this stage, a data analyst must decide what kind of information to present by using their discretion with respect to relevance. For example, deciding what kind of information a share trader would want to know vs what kind of information a dealer in the commodity market would want to know. Document structuring: In this stage, a user will have to decide the sequence, format of content and the desired template. For example, to decide the order of large cap, mid cap, small cap shares while writing a narrative about equity movement in the stock market. Aggregation: No repetition is the basic rule of any report writing. To keep it simple and improve readability, merging sentences, omitting repetitive words, phrases etc, falls under this stage. For example, if NLG software is writing a report on sales and there is no substantial change in volume of sales for a few months, there are chances NLG software might write repetitive paragraphs for no substantial information. You will then have to condense it in a way it does not become long and boring. Lingual choice: Deciding what words to use exactly to describe particular concepts. For example, deciding whether to use the word ‘medium’ or ‘moderate’ while describing a change. Best software products available for natural language generation There are a variety of software products available to help you get started with Natural Language Generation. Quill, Syntheses, Arria, Amazon Polly, Yseop are popular ones. You can make a decision based on the industry you are operating in, for the department you will be deploying the tool, exact nature of report creation, etc. Let us see what kind of aid does these programs offer to the businesses. Yseop: Yseop Compose’s Natural Language Generation software enables data-driven decision making by explaining insights in a plain language. Yseop Compose is the only multilingual Natural Language Generation software and hence truly global. Amazon Polly: It is a software that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products. Arria: Arria NLG Platform is the one that integrates cutting-edge techniques in data analytics, artificial intelligence and computational linguistics. It analyses large and diverse data sets and automatically writes tailored, actionable reports on what’s happening within that data, with no human intervention, at vast scale and speed. Quill: It is an advanced NLG platform which comprehends user intent and performs relevant data analysis to deliver Intelligent Narratives—automated stories full of audience-relevant, insightful information. Synthesys: It is one of the popular NLG software products that scans through all data and highlights the important people, places, organizations, events and facts being discussed, resolve highlighted points and determines what’s important, connecting the dots together and figures out what the final picture means by comparing it with the opportunities, risks and anomalies users are looking for. Natural Language Generation tools automate analysis and increase the efficacy of Business Intelligence tools. Rather than generating charts and tables, NLG tools interpret the data and draft analysis in a written form that communicates precisely what’s important to know. These tools perform regular analysis of predefined data sets, eliminate the manual efforts required to draft reports and the skilled labour required to analyse and interpret the results. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau What are the best resources to learn Natural Language Generation? Gartner, a leading research and advisory company forecasts that most companies will have to employ a Chief Data officer by 2019. With the gigantic amount of data available, it is important to decide which information can add business value, drive efficiency and improve risk management. This will be the responsibility of Data Officers. With increasing global demand for the profession, there can be no better time to learn about Natural Language Generation which is a critical part of Data Science and Artificial Intelligence. Though Natural Language generation has a huge scope, there are very few comprehensive academic programs designed to train candidates to be future ready. However, with a great vision, UpGrad offers a PG Diploma in Machine Learning and AI, in partnership with IIIT-Bangalore, which aims to build highly skilled professionals in India to cater to the increasing global demand. It gives you a chance to learn from a comprehensive collection of case-studies, hand-picked by industry experts, to give you an in-depth understanding of how Machine Learning & Artificial Intelligence impact industries like Telecom, Automobile, Finance & more. What are you waiting for? Don’t let go of this wonderful opportunity, start exploring today!
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by Maithili Pradhan

30 Jan'18
A Beginner’s Guide To Natural Language Understanding

8.29K+

A Beginner’s Guide To Natural Language Understanding

“A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.” – Alan Turing Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses The entire gamut of artificial intelligence is based on machines being able to ‘understand’ and ‘respond’ to human beings. Which is impossible without the capability of machines to interact with humans in their natural language, like other human beings. Moreover, understanding does not involve the mere exchange of information and data but an exchange of emotions, feelings, ideas and intent. Can machines ever do that? Well, the answer is affirmative and it is not even that surprising anymore. What is this miraculous technology that smoothly facilitates the interaction between humans and machines? It is Natural Language Understanding. What is Natural Language Understanding? Natural Language Understanding is a part of Natural Language Processing. It undertakes the analysis of content, text-based metadata and generates summarized content in natural, human language. It is opposite to the process of Natural Language Generation. NLG deals with input in the form of data and generates output in the form of plain text while Natural Language Understanding tools process text or voice that is in natural language and generates appropriate responses by summarizing, editing or creating vocal responses. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Get Machine Learning Certification from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Natural Language Understanding Vs Natural Language Processing Natural Language Processing is a wide term which includes both Natural Language Understanding and Natural Language Generations along with many other techniques revolving around translating and analysing natural language by machines to perform certain commands.    Examples of Natural Language Processing Natural Language Processing is everywhere and we use it in our daily lives without even realising it. Do you know how spam messages are separated from your emails? Or autocorrect and predictive typing that saves so much of our time, how does that happen? Well, it is all part of Natural Language Processing. Here are some examples of Natural Language Processing technologies used widely: Intelligent personal assistants – We are all familiar with Siri and Cortana. These mobile software products that perform tasks, offer services, with a combination of user input, location awareness, and the ability to access information from a variety of online sources are undoubtedly one of the biggest achievements of natural language processing. Machine translation – To read a description of a beautiful picture on Instagram or to read updates on Facebook, we all have used that ‘see translation’ command at least once. And google translation services helps in urgent situations or sometimes just to learn few new words. These are all examples of machine translations, where machines provide us with translations from one natural language to another. Speech recognition – Converting spoken words into data is an example of natural language processing. It is used for multiple purposes like dictating to Microsoft Word, voice biometrics, voice user interface, etc. Affective computing – It is nothing but emotional intelligence training for machines. They learn to understand your emotions, feelings, ideas to interact with you in more humane ways. Natural language generation – Natural language generation tools scan structured data, undertake analysis and generate information in text format produced in natural language. Natural language understanding – As explained above, it scans content written in natural languages and generates small, comprehensible summaries of text. Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Best tools for Natural Language Understanding available today Natural Language Processing deals with human language in its most natural form and on a real-time basis, as it appears in social media content, emails, web pages, tweets, product descriptions, newspaper articles, and scientific research papers, etc, in a variety of languages. Businesses need to keep a tab on all this content, constantly. Here are a few popular natural language understanding software products which effectively aid them in this daunting task. Wolfram – Wolfram Alpha is an answer engine developed by Wolfram Alpha LLC (a subsidiary of Wolfram Research). It is an online service that provides answers to factual questions by computing the answer from externally sourced, “curated data”. Natural language toolkit – The Natural Language Toolkit, also known as NLTK, is a suite of programs used for symbolic and statistical natural language processing (NLP) for the English language. It is written in the Python programming language and was developed by Steven Bird and Edward Loper at the University of Pennsylvania. Stanford coreNLP – Stanford CoreNLP is an annotation-based NLP pipeline that offers core natural language analysis. The basic distribution provides model files for the analysis of English, but the engine is compatible with models for other languages. GATE (General Architecture for Text Engineering) – It offers a wide range of natural language processing tasks. It is a mature software used across industries for more than 15 years. Apache openNLP – The Apache OpenNLP is a toolkit based on machine learning to process natural language text. It is written in Java and is produced by Apache software foundation. It offers services like tokenizers, chucking, parsing, part of speech tagging, sentence segmentation, etc. Applications of Natural Language Understanding As we have already seen, natural language understanding is basically nothing but a smart machine reading comprehension. Now let’s have a close look at how it is used to promote the efficiency and accuracy, while saving time and efforts, of human resources, which can then be put to better use. Collecting data and data analysis – To be able to serve well, a business must know what is expected out of them. Data on customer feedback is not numeric data like sales or financial statements. It is open-ended and text heavy. For companies to identify patterns and trends throughout, this data and taking action as per identified gaps or insights, is crucial for survival and growth. More and more companies are realizing that implementing a natural language understanding solution provides strong benefits to analysing metadata like customer feedback and product reviews. Natural language understanding in such cases proves to be more effective and accurate than traditional methods like hand-coding. It helps the customer’s voice to reach you clearer and faster, which leads to effective strategizing and productive implementation. Reputation monitoring –  Customer feedback is just a tip of the iceberg as compared to the real feelings of customers about the brand. As customers, we hardly participate in customer survey feedbacks. Most of the real customer sentiments hence are trapped in unstructured data. News, blog posts, chats, and social media updates contain huge amounts of such data which is more natural and can be used to know the ‘real’ feelings of customers about the product or service. Natural language understanding software products help businesses to scan through such scattered data and draw practical inferences. Customer service – Natural Language Understanding is able to communicate with untrained individuals and can understand their intent. NLU is capable of understanding the meaning in spite of some human errors like mispronunciations or transposed letters or words. It also uses algorithms that break down human speech to structured ontology and fishes out the meaning, intent, sentiment, and the crux of human speech. One of the most important goals of NLU is to create chatbots or human interacting bots that can effectively communicate with humans without any human supervision. There are various software products like Nuance which are already involved in customer interaction. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Automated trading – Capital market trading automation is not a new phenomenon anymore. Multiple software products and platforms are now available that analyse market movements, the profile of industries and financial strength of a company and based on technical analysis design the trading patterns. Advanced Natural Language Understanding tools which scan through various sources like financial statements, reports, market news are the basis of automated trading systems. Market Intelligence – “What are competitors doing?” is one of the most critical information businesses need on a real-time basis. Information influences markets. Information exchange between various stakeholders designs and redesigns market dynamics all the time. Keeping a close watch on the status of an industry is essential to developing a powerful strategy, but the channels of content distribution today (RSS feeds, social media, emails) generate so much information that it’s been increasingly difficult to keep a tab on such unstructured, multi-sourced content. Financial markets have started using natural language understanding tools rigorously to keep track of information exchange in the market and help them reach it immediately. Due to such varied functions carried out by natural language understanding programs, its importance in trade, business, commerce and the industry is ever increasing. It is a smart move to learn natural language understanding programs to ensure yourself a successful career. What is the best way to learn Natural Language Understanding? The best way to prepare yourself for a brighter future in technological endeavors is to understand the algorithms of Artificial intelligence. The Post Graduate Diploma in Machine Learning and AI by UpGrad offers a chance to master concepts like Neural Networks, Natural Language Processing, Graphical Models and Reinforcement Learning. The most unique aspect of this course is the career support. And, the industry mentorship, which will help you prepare yourself for intense competition in the industry, within your actual job. So, let’s learn to use software products widely used in industry mentioned earlier like NLKT. This program aims at producing well-rounded data scientists and AI professionals with thorough knowledge of mathematics, expertise in relevant tools/languages and understanding of cutting-edge algorithms and applications. Start preparing today for a better tomorrow! Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
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by Maithili Pradhan

30 Jan'18
Neural Networks for Dummies: A Comprehensive Guide

10.94K+

Neural Networks for Dummies: A Comprehensive Guide

Our brain is an incredible pattern-recognizing machine. It processes ‘inputs’ from the outside world, categorizes them (that’s a dog; that’s a slice of pizza; ooh, that’s a bus coming towards me!), and then generates an ‘output’ (petting the dog; the yummy taste of that pizza; getting out of the way of the bus!). Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses All of this with little conscious effort, almost impulsively. It’s the very same system that senses if someone is mad at us, or involuntarily notices the stop signal as we speed past it. Psychologists call this mode of thinking ‘System 1’, and it includes innate skills — like perception and fear — that we share with other animals. (There’s also a ‘System 2’, to know more about it, check out the extremely informative Thinking, Fast and Slow by Daniel Kahneman). How is all of this related to Neural Networks, you ask? Wait, we’ll get there in a second. Look at the image above, just your regular numbers, distorted to help you explain the learning of Neural Networks better. Even looking cursorily, your mind will prompt you with the words “192”. You surely didn’t go “Ah, that seems like a straight line, I think it’s a 1”. You didn’t compute it – it happened instantly. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Fascinating, right? There is a very simple reason for this – you’ve come across the digit so many times in your life, that by trial and error, your brain automatically recognizes the digit if you present it with something even remotely close to it. Let’s cut to the chase. What exactly is a Neural Network? How does it work? By definition, a neural network is a system of hardware or softwares, patterned after the working of neurons in the human brain. Basically, it helps computers think and learn like humans. An example will make this clearer: As a child, if we ever touched a hot coffee mug and it burnt us, we made sure not to touch a hot mug ever again. But did we have any such concept of hurt in our conscience BEFORE we touched it? Not really. This adjustment of our knowledge and understanding of the world around us is based on recognizing patterns. And, like us, computers, too, learn through the same type of pattern recognition. This learning forms the whole basis of the working of neural networks. Traditional computer programs work on logic trees – If A happens, then B happens. All the potential outcomes for each of the systems can be preprogrammed. However, this eliminates the scope of flexibility. There’s no learning there. And that’s where Neural Networks come into the picture! A neural network is built without any specific logic. Essentially, it is a system that is trained to look for and adapt to, patterns within data. It is modeled exactly after how our own brain works. Each neuron (idea) is connected via synapses. Each synapse has a value that represents the probability or likelihood of the connection between two neurons to occur. Take a look at the image below: What exactly are neurons, you ask? Simply put, a neuron is just a singular concept. A mug, the colour white, tea -, the burning sensation of touching a hot mug, basically anything. All of these are possible neurons. All of them can be connected, and the strength of their connection is decided by the value of their synapse. Higher the value, better the connection. Let’s see one basic neural network connection to make you understand better: Each neuron is the node and the lines connecting them are synapses. Synapse value represents the likelihood that one neuron will be found alongside the other. So, it’s pretty clear that the diagram shown in the above image is describing a mug containing coffee, which is white in colour and is extremely hot. All mugs do not have the properties like the one in question. We can connect many other neurons to the mug. Tea, for example, is likely more common than coffee. The likelihood of two neurons being connected is determined by the strength of the synapse connecting them. Greater the number of hot mugs, the stronger the synapse. However, in a world where mugs are not used to hold hot beverages, the number of hot mugs would decrease drastically. Incidentally, this decrease would also result in lowering the strength of the synapses connecting mugs to heat. So, Becomes This small and seemingly unimportant description of a mug represents the core construction of neural networks. We touch a mug kept on a table — we find that it’s hot. It makes us think all mugs are hot. Then, we touch another mug – this time, the one kept on the shelf – it’s not hot at all. We conclude that mugs in the shelf aren’t hot. As we grow, we evolve. Our brain has been taking in data all this time. This data makes it determine an accurate probability as to whether or not the mug we’re about to touch will be hot. Neural Networks learn in the exact same way. Now, let’s talk a bit aboutthe first and the most basic model of a neural network: The Perceptron! What is a Perceptron? A perceptron is the most basic model of a neural network. It takes multiple binary inputs: x1, x2, …, and produces a single binary output. Let’s understand the above neural network better with the help of an analogy. Say you walk to work. Your decision of going to work is based on two factors majorly: the weather, and whether it is a weekday or not. The weather factor is still manageable, but working on weekends is a big no! Since we have to work with binary inputs, let’s propose the conditions as yes or no questions. Is the weather fine? 1 for yes, 0 for no. Is it a weekday? 1 yes, 0 no. Remember, we cannot explicitly tell the neural network these conditions; it’ll have to learn them for itself. How will it decide the priority of these factors while making a decision? By using something known as “weights”. Weights are just a numerical representation of the preferences. A higher weight will make the neural network consider that input at a higher priority than the others. This is represented by the w1, w2…in the flowchart above. “Okay, this is all pretty fascinating, but where do Neural Networks find work in a practical scenario?” Real-life applications of Neural Networks If you haven’t yet figured it out, then here it is, a neural network can do pretty much everything as long as you’re able to get enough data and an efficient machine to get the right parameters. Anything that even remotely requires machine learning turns to neural networks for help. Deep learning is another domain that makes extensive use of neural networks. It is one of the many machine learning algorithms that enables a computer to perform a plethora of tasks such as classification, clustering, or prediction. With the help of neural networks, we can find the solution of such problems for which a traditional-algorithmic method is expensive or does not exist. Neural networks can learn by example, hence, we do not need to program it to a  large extent. Neural networks are accurate and significantly faster than conventional speeds. Because of the reasons mentioned above and more, Deep Learning, by making use of Neural Networks, finds extensive use in the following areas: Speech recognition: Take the example of Amazon Echo Dot – magic speakers that allow you to order food, get news and weather updates, or simply buy something online just by talking it out. Handwriting recognition: Neural networks can be trained to understand the patterns in somebody’s handwriting. Have a look at Google’s Handwriting Input application – which makes use of handwriting recognition to seamlessly convert your scribbles into meaningful texts. Face recognition: From improving the security on your phone (Face ID) to the super-cool Snapchat filters – face recognition is everywhere. If you’ve ever uploaded a photo on Facebook and were asked to tag the people in your photo, you know what face recognition is! Providing artificial intelligence in games: If you’ve ever played chess against a computer, you already know how artificial intelligence powers games and game development. It’s to the extent that players use AI to improve upon their tactics and try their strategies first-hand. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau In Conclusion… Neural networks form the backbone of almost every big technology or invention you see today. It’s only fair to say that imagining deep/machine learning without neural networks is next to impossible. Depending on the way you implement a network and the kind of learning you put to use, you can achieve a lot out of a neural network, as compared to a traditional computer system. Learn ML courses from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
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by Reetesh Chandra

06 Feb'18
Neural Networks: Applications in the Real World

20.03K+

Neural Networks: Applications in the Real World

Neural Networks find extensive applications in areas where traditional computers don’t fare too well. Like, for problem statements where instead of programmed outputs, you’d like the system to learn, adapt, and change the results in sync with the data you’re throwing at it. Neural networks also find rigorous applications whenever we talk about dealing with noisy or incomplete data. And honestly, most of the data present out there is indeed noisy. Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses With their brain-like ability to learn and adapt, Neural Networks form the entire basis and have applications in Artificial Intelligence, and consequently, Machine Learning algorithms. Before we get to how Neural Networks power Artificial Intelligence, let’s first talk a bit about what exactly is Artificial Intelligence. For the longest time possible, the word “intelligence” was just associated with the human brain. But then, something happened! Scientists found a way of training computers by following the methodology our brain uses. Thus came Artificial Intelligence, which can essentially be defined as intelligence originating from machines. To put it even more simply, Machine Learning is simply providing machines with the ability to “think”, “learn”, and “adapt”. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses With so much said and done, it’s imperative to understand what exactly are the use cases of AI, and how Neural Networks help the cause. Let’s dive into the applications of Neural Networks across various domains – from Social Media and Online Shopping, to Personal Finance, and finally, to the smart assistant on your phone. You should remember that this list is in no way exhaustive, as the applications of neural networks are widespread. Basically, anything that makes the machines learn is deploying one or the other type of neural network. Social Media The ever-increasing data deluge surrounding social media gives the creators of these platforms the unique opportunity to dabble with the unlimited data they have. No wonder you get to see a new feature every fortnight. It’s only fair to say that all of this would’ve been like a distant dream without Neural Networks to save the day. FYI: Free Deep Learning Course! Neural Networks and their learning algorithms find extensive applications in the world of social media. Let’s see how: Facebook As soon as you upload any photo to Facebook, the service automatically highlights faces and prompts friends to tag. How does it instantly identify which of your friends is in the photo? The answer is simple – Artificial Intelligence. In a video highlighting Facebook’s Artificial Intelligence research, they discuss the applications of Neural Networks to power their facial recognition software. Facebook is investing heavily in this area, not only within the organization, but also through the acquisitions of facial-recognition startups like Face.com (acquired in 2012 for a rumored $60M), Masquerade (acquired in 2016 for an undisclosed sum), and Faciometrics (acquired in 2016 for an undisclosed sum). In June 2016, Facebook announced a new Artificial Intelligence initiative that uses various deep neural networks such as DeepText – an artificial intelligence engine that can understand the textual content of thousands of posts per second, with near-human accuracy. Instagram Instagram, acquired by Facebook back in 2012, uses deep learning by making use of a connection of recurrent neural networks to identify the contextual meaning of an emoji – which has been steadily replacing slangs (for instance, a laughing emoji could replace “rofl”). By algorithmically identifying the sentiments behind emojis, Instagram creates and auto-suggests emojis and emoji related hashtags. This may seem like a minor application of AI, but being able to interpret and analyze this emoji-to-text translation at a larger scale sets the basis for further analysis on how people use Instagram. Pinterest Pinterest uses computer vision – another application of neural networks, where we teach computers to “see” like a human, in order to automatically identify objects in images (or “pins”, as they call it) and then recommend visually similar pins. Other applications of neural networks at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing. Online Shopping Do you find yourself in situations where you’re set to buy something, but you end up buying a lot more than planned, thanks to some super-awesome recommendations? Yeah, blame neural networks for that. By making use of neural network and its learnings, the e-commerce giants are creating Artificial Intelligence systems that know you better than yourself. Let’s see how: Search Your Amazon searches (“earphones”, “pizza stone”, “laptop charger”, etc) return a list of the most relevant products related to your search, without wasting much time. In a description of its product search technology, Amazon states that its algorithms learn automatically to combine multiple relevant features. It uses past patterns and adapts to what is important for the customer in question. And what makes the algorithms “learn”? You guessed it right – Neural Networks! Recommendations Amazon shows you recommendations using its “customers who viewed this item also viewed”,  “customers who bought this item also bought”, and also via curated recommendations on your homepage, on the bottom of the item pages, and through emails. Amazon makes use of Artificial Neural Networks to train its algorithms to learn the pattern and behaviour of its users. This, in turn, helps Amazon provide even better and customized recommendations. Banking/Personal Finance Cheque Deposits Through Mobile Most large banks are eliminating the need for customers to physically deliver a cheque to the bank by offering the ability to deposit cheques through a smartphone application. The technologies that power these applications use Neural Networks to decipher and convert handwriting on checks into text. Essentially, Neural Networks find themselves at the core of any application that requires handwriting/speech/image recognition. Fraud Prevention How can a financial institution determine a fraudulent transaction? Most of the times, the daily transaction volume is too much to be reviewed manually. To help with this, Artificial Intelligence is used to create systems that learn through training what types of transactions are fraudulent (speak learning, speak Neural Networks!). FICO – the company that creates credit ratings that are used to determine creditworthiness, makes use of neural networks to power their Artificial Intelligence to predict fraudulent transactions. Factors that affect the artificial neural network’s final output include the frequency and size of the transaction and the kind of retailer involved. Powering Your Mobile Phones Voice-to-Text One of the more common features on smartphones today is voice-to-text conversion. Simply pressing a button or saying a particular phrase (“Ok Google”, for example), lets you start speaking to your phone and your phone converts the audio into text. Google makes use of artificial neural networks in recurrent connection to power voice search. Microsoft also claims to have developed a speech-recognition system – using Neural Networks, that can transcribe conversations slightly more accurately than humans. Smart Personal Assistants With the voice-to-text technology becoming accurate enough to rely on for basic conversations, it is turning into the control interface for a new generation of personal assistants. Initially, there were simpler phone assistants – Siri and Google Now (now succeeded by the more sophisticated Google Assistant), which could perform internet searches, set reminders, and integrate with your calendar. Amazon expanded upon this model with the announcement of complementary hardware and software components – Alexa, and Echo (later, Dot). Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau To Wrap Up… We’ve only scratched the surface when it comes to the applications of neural networks in day-to-day life. Specific industries and domains have specific interactions with Artificial Intelligence by making use of neural networks which is far beyond what’s talked about in this article. For example, chess players regularly use chess engines to analyze their games, improve themselves, and practice new tactics – and it goes without saying that the chess engine in question deploys Neural Networks to accomplish the learning. Learn ML courses Online from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Do you have any other interesting real-life use case of Neural Networks that we might have missed? Drop it in the comments below!
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by Reetesh Chandra

06 Feb'18
Go and the Challenge to Artificial General Intelligence

5.78K+

Go and the Challenge to Artificial General Intelligence

This article aims to explore the connection between the game ‘Go’ and artificial intelligence. The objective is to answer the questions – What makes the game of Go, special? Why was mastering the game of Go difficult for a computer? Why was a computer program able to beat a chess grandmaster in 1997? Why did it take close to two decades to crack Go? Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses “Gentlemen should not waste their time on trivial games – they should study Go” – Confucius In fact, artificial intelligence pundits thought computers would only be able to beat a world Go champion by 2027. Thanks to DeepMind, an artificial intelligence company under the umbrella of Google, this formidable task was achieved a decade earlier. This article will talk about the technologies used by DeepMind to beat the world Go champion. Finally, this post discusses how this technology can be used to resolve some complex, real-world problems. Go – What is it? Go is a 3000-year-old Chinese strategy board game, which has retained its popularity through the ages. Played by tens of millions of people worldwide, Go is a two-player board game with simple rules and intuitive strategy. Different board sizes are in use for playing this game; professionals use a 19×19 board. The game starts with an empty board. Each player then takes turns to place the black and white stones (black goes first) on the board, at the intersection of the lines (unlike chess, where you place pieces in the squares). A player can capture the stones of the opponent by surrounding it from all sides. For each captured stone, some points are awarded to the player. The objective of the game is to occupy maximum territory on the board along with capturing your opponents’ stones. In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Go is about creation, unlike Chess, which is about destruction. Go requires freedom, creativity, intuition, balance, strategy and intellectual depth to master the game. Playing Go involves both sides of the brain. In fact, the brain scans of Go players have revealed that Go helps in brain development by improving connections between both the brain hemispheres. Go and the Challenge to Artificial Intelligence (AI) Computers were able to master Tic-Tac-Toe in 1952. Deep Blue was able to beat Chess grandmaster Garry Kasparov in 1997. The computer program was able to win against the world champion in Jeopardy (a popular American game) in 2001. DeepMind’s AlphaGo was able to defeat a world Go champion in 2016. Why is it considered challenging for a computer program to master the game of Go? Chess is played on an 8×8 board whereas Go uses a 19×19 size board. In the opening of a chess game, a player will have 20 possible moves. In a Go opening, a player can have 361 possible moves.The number of possible Go board positions is equal to 10 to the power 170; more than the number of atoms in our universe! The potential number of board positions makes Go googol times (10 to the power 100) more complex than chess. In chess, for each step, a player is faced with a choice of 35 moves. On average, a Go player will have 250 possible moves at each step. In Chess, at any given position, it is relatively easy for a computer to do brute force search and choose the best possible move which maximises the chances of winning. A brute force search is not possible in the case of Go, as the potential number of legal moves allowed for each step is humongous. For a computer to master chess, it becomes easier as the game progresses because the pieces are removed from the board. In Go, it becomes more difficult for the computer program as stones are added to the board as the game progresses. Typically, a Go game will last 3 times longer than a game of chess. Due to all these reasons, a top computer Go program was only able to catch up with the Go world champion in 2016, after a huge explosion of new machine learning techniques. Scientists working at DeepMind were able to come up with a computer program called AlphaGo which defeated world champion Lee Seedol. Achieving the task was not easy. The researchers at DeepMind came up with many novel innovations in the process of creating AlphaGo. “The rules of Go are so elegant, organic, and rigorously logical that if intelligent life forms exist elsewhere in the universe, they almost certainly play Go.” – Edward Laskar How AlphaGo Works AlphaGo is a general purpose algorithm, which means it can be put to use for solving other tasks as well. For example, Deep Blue from IBM is specifically designed for playing chess. Rules of chess together with the accumulated knowledge from centuries of playing the game are programmed into the brain of the program. Deep Blue can’t be used even for playing trivial games like Tic-Tac-Toe. It can do only one specific thing, which it is very good at, i.e. playing chess. AlphaGo can learn to play other games as well apart from Go. These general purpose algorithms constitute a novel field of research, called Artificial General Intelligence. AlphaGo uses state-of-the-art methods – Deep Neural Networks (DNN), Reinforcement Learning (RL), Monte Carlo Tree Search (MCTS), Deep Q Networks (DQN) (a novel technique introduced and popularised by DeepMind which combines neural networks with reinforcement learning), to name a few. It then combines all these methods innovatively to achieve superhuman level mastery in the game of Go. Let’s first look at each individual piece of this puzzle before going into how these pieces are tied together to achieve the task at hand. Deep Neural Networks DNNs are a technique to perform machine learning, loosely inspired by the functioning of the human brain. A DNN’s architecture consists of layers of neurons. DNN can recognise patterns in data without being explicitly programmed for it. It maps the inputs to outputs without anyone specifically programming it for the same. As an example, let us assume that we have fed the network with a lot of cat and dog photos. At the same time, we are also training the system by telling it (in the form of labels) if a particular image is of a cat or a dog (this is called supervised learning). A DNN will learn to recognise the pattern from the photos to successfully differentiate between a cat and a dog. The main objective of the training is that when a DNN sees a new picture of either a dog or a cat, it should be able to correctly classify it, i.e. predict if it is a cat or a dog. Let us understand the architecture of a simple DNN. The number of neurons in the input layer corresponds to the size of the input. Let us assume our cat and dog photos are a 28×28 image. Each row and column will consist of 28 pixels each, which makes it a total of 784 pixels for each picture. In such a case the input layer will comprise of 784 neurons, one for each pixel. The number of neurons in the output layer will depend on the number of classes into which the output needs to be classified. In this case, the output layer will consist of two neurons – one corresponding to ‘cat’, the other to ‘dog’. There will be many neuron layers in between the input and output layers (which is the origin of using the term ‘Deep’ in ‘Deep Neural Network’). These are called “hidden layers”. The number of hidden layers and the number of neurons in each layer is not fixed. In fact, changing these values is exactly what leads to optimisation of performance. These values are called hyper-parameters, and they need to be tuned according to the problem at hand. The experiments surrounding neural networks largely involve finding out the optimal number of hyperparameters. The training phase of DNNs will consist of a forward pass and a backward pass. First, all the connections between the neurons are initialised with random weights. During the forward pass, the network is fed with a single image. The inputs (pixel data from the image) are combined with the parameters of the network (weights, biases and activation functions) and feed-forwarded through hidden layers, all the way to the output, which returns a probability of a photo belonging to each of the classes. Then, this probability is compared with the actual class label, and an “error” is calculated. At this point, the backward pass is performed – this error information is passed back through the network through a technique called “back-propagation”. During initial phases of training, this error will be high, and a good training mechanism will gradually reduce this error. The DNNs are trained in this way with a forward and backward pass until the weights stop changing (this is known as convergence). Then the DNNs will be able to predict and classify the images with a high degree of accuracy, i.e. whether the picture has a cat or a dog. Research has given us many different Deep Neural Network Architectures. For Computer Vision problems (i.e. problems involving images), Convolution Neural Networks (CNNs) have traditionally given good results. For issues which involve a sequence – speech recognition or language translation – Recurrent Neural Networks (RNN) provide excellent results. In the case of AlphaGo, the process was as follows: first, the Convolution Neural Network (CNN) was trained on millions of images of board positions. Next, the network was informed about the subsequent move played by the human experts in each case during the training phase of the network. In the same manner as earlier mentioned, the actual value was compared with the output and some sort of “error” metric was found. At the end of the training, the DNN will output the next moves along with probabilities which are likely to be played by an expert human player. This kind of network can only come up with a step which is played by a human expert player. DeepMind was able to achieve an accuracy of 60% in predicting the move that the human would make. However, to beat a human expert at Go, this is not sufficient. The output from the DNN is further processed by Deep Reinforcement Network, an approach conceived by DeepMind, which combines deep neural networks and reinforcement learning. Deep Reinforcement Learning Reinforcement learning (RL) is not a new concept. Nobel prize laureate Ivan Pavlov experimented on classical conditioning on dogs and discovered the principles of reinforcement learning in 1902. RL is also one of the methods with which humans learn new skills. Ever wondered how the Dolphins in shows are trained to jump to such great heights out of the water? It is with the help of RL. First, the rope which is used for preparing the dolphins is submerged in the pool. Whenever the dolphin crosses the cable from the top, it is rewarded with food. When it does not cross the rope the reward is withdrawn. Slowly the dolphin will learn that it is paid whenever it passes the cord from above. The height of the rope is increased gradually to train the dolphin. Agents in reinforcement learning are also trained using the same principle. The agent will take action and interact with the environment. The action taken by the agent causes the environment to change. Further, the agent received feedback about the environment. The agent is either rewarded or not, depending on its action and the objective at hand. The important point is, this objective at hand is not explicitly stated for the agent. Given sufficient time, the agent will learn how to maximise future rewards. Combining this with DNNs, DeepMind invented Deep Reinforcement Learning (DRL) or Deep Q Networks (DQN) where Q stands for maximum future rewards obtained. DQNs were first applied to Atari games. DQN learnt how to play different types of Atari games just out of the box. The breakthrough was that no explicit programming was required for representing different kinds of Atari games. A single program was smart enough to learn about all the different environments of the game, and through self-play, was able to master many of them. In 2014, DQN outperformed previous machine learning methods in 43 of the 49 games (now it has been tested on more than 70 games). In fact, in more than half the games, it performed at more than 75% of the level of a professional human player. In certain games, DQN even came up with surprisingly far-sighted strategies that allowed it to achieve the maximum attainable score—for example, in Breakout, it learned to first dig a tunnel at one end of the brick wall, so the ball would bounce around the back and knock out bricks from behind. Policy and Value Networks There are two main types of networks inside AlphaGo: One of the objectives of AlphaGo’s DQNs is to go beyond the human expert play and mimic new innovative moves, by playing against itself millions of times and thereby incrementally improving the weights. This DQN had an 80% win rate against common DNNs. DeepMind decided to combine these two neural networks (DNN and DQN) to form the first type of network – a ‘Policy Network’. Briefly, the job of a policy network is to reduce the breadth of the search for the next move and to come up with a few good moves which are worth further exploration. Once the policy network is frozen, it plays against itself millions of times. These games generate a new Go dataset, consisting of the various board positions and the outcomes of the games. This dataset is used to create an evaluation function. The second type of function – the ‘Value Network’ is used to predict the outcome of the game. It learns to take various board positions as inputs and predict the outcome of the game and the measure of it. Combining the Policy and Value Networks After all this training, DeepMind finally ended up with two neural networks – Policy and Value Networks. The policy network takes the board position as an input and outputs the probability distribution as the likelihood of each of the moves in that position. The value network again takes the position of the board as input and outputs a single real number between 0 and 1. If the output of the network is zero, it means that white is completely winning and 1 indicates a complete win for the player with black stones. The Policy network evaluates current positions, and the value network evaluates future moves. The division of tasks into these two networks by DeepMind was one of the major reasons behind the success of AlphaGo. Combining Policy and Value networks with Monte Carlo Tree Search (MCTS) and Rollouts The neural networks on their own will not be enough. To win the game of Go, some more strategising is required. This plan is achieved with the help of MCTS. Monte Carlo Tree Search also helps in stitching the two neural networks together in an innovative way. Neural networks assist in an efficient search for the next best move. Let’s try constructing an example which will help you visualise all of this much better. Imagine that the game is in a new position, one which has not been encountered before. In such a situation, a policy network is called upon to evaluate the current situation and possible future paths; as well as the desirability of the paths and the value of each move by the Value networks, supported by Monte Carlo rollouts. Policy network finds all the possible “good” moves and value networks evaluate each of their outcomes. In Monte Carlo rollouts, a few thousand random games are played from the positions recognised by the policy network. Experiments were done to determine the relative importance of value networks against Monte Carlo rollouts. As a result of this experimentation, DeepMind assigned 80% weightage to the Value networks and 20% weightage to the Monte Carlo rollout evaluation function. The policy network reduces the width of the search from 200-odd possible moves to the 4 or 5 best moves. The policy network expands the tree from these 4 or 5 steps which need consideration. The value network helps in cutting down the depth of the tree search by instantly returning the outcome of the game from that position. Finally, the move with the highest Q value is selected, i.e. the step with maximum benefit. “The game is played primarily through intuition and feel, and because of its beauty, subtlety and intellectual depth it has captured the human imagination for centuries.” – Demis Hassabis Application of AlphaGo to real-world problems The vision of DeepMind, from their website, is very telling – “Solve intelligence. Use this knowledge to make the world a better place”. The end goal of this algorithm is to make it general-purpose so that it can be used to solve complex real-world problems. DeepMind’s AlphaGo is a significant step forward in the quest for AGI. DeepMind has used its technology successfully to solve real-world problems – let’s look at some examples: Reduction in energy consumption DeepMind’s AI was successfully utilised to reduce Google’s data centre cooling cost by 40%. In any large-scale energy consuming environment this improvement is a phenomenal step forward. One of the primary sources of energy consumption for a data centre is cooling. A lot of heat generated from running the servers needs to be removed for keeping it operational. This is accomplished by large-scale industrial equipment like pumps, chillers and cooling towers. As the environment of the data centre is very dynamic, it is challenging to operate at optimal energy efficiency. DeepMind’s AI was used to tackle this problem. First, they proceeded using historical data, which was collected by thousands of sensors within the data centre. Using this data, they trained an ensemble of DNNs on average future Power Usage Effectiveness (PUE). As this is a general-purpose algorithm, it is planned that it will be applied to other challenges as well, in the data centre environment. The possible applications of this technology include getting more energy from the same unit of input, reducing semiconductor manufacturing energy and water usage, etc. DeepMind announced in its blog post that this knowledge would be shared in a future publication so that other data centres, industrial operators and ultimately the environment can greatly benefit from this significant step. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau Radiotherapy planning for head and neck cancers DeepMind has collaborated with the radiotherapy department at University College London Hospital’s NHS Foundation Trust, a world leader in cancer treatment. One in 75 men and one in 150 women are diagnosed with oral cancer in their lifetime. Due to the sensitive nature of the structures and organs in the head and neck area, radiologists need to take extreme care while treating them. Before radiotherapy is administered, a detailed map needs to be prepared with the areas to be treated and the areas to be avoided. This is known as segmentation. This segmented map is fed into the radiography machine, which will then target cancer cells without harming healthy cells. In the case of cancer of the head or neck region, this is a painstaking job for the radiologists as it involves very sensitive organs. It takes around four hours for the radiologists to create a segmented map for this area. DeepMind, through its algorithms, is aiming to reduce the time required for generating the segmented maps, from four to one hour. This will significantly free up the radiologist’s time. More importantly, this segmentation algorithm can be utilised for other parts of the body. To summarise, AlphaGo successfully beat the 18-time world Go champion, Lee Seedol, four times in a best-of-five tournament in 2016. In 2017, it even beat a team of the world’s best players. It uses a combination of DNN and DQN as a policy network for coming up with the next best move, and one DNN as a value network to evaluate the outcome of the game. Monte Carlo tree search is used along with both the policy and value networks to reduce the width and depth of the search – they are used to improve the evaluation function. The ultimate aim of this algorithm is not to solve board games but to invent an Artificial General Intelligence algorithm. AlphaGo is undoubtedly a big step ahead in that direction. Of course, there have been other effects. As the news of AlphaGo Vs Lee Seedol became viral, the demand for Go boards jumped tenfold. Many stores reported instances of Go boards going out of stock, and it became challenging to purchase a Go board. Fortunately, I just found one and ordered it for myself and my kid. Are you planning to buy the board and learn Go? Learn ML courses from the World’s top Universities. 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Sentiment Analysis: What is it and Why Does it Matter?

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Sentiment Analysis: What is it and Why Does it Matter?

Sentiment Analysis, also known as Opinion Mining, refers to the techniques and processes that help organisations retrieve information about how their customer-base is reacting to a particular product or service. Best Machine Learning and AI Courses Online Master of Science in Machine Learning & AI from LJMU Executive Post Graduate Programme in Machine Learning & AI from IIITB Advanced Certificate Programme in Machine Learning & NLP from IIITB Advanced Certificate Programme in Machine Learning & Deep Learning from IIITB Executive Post Graduate Program in Data Science & Machine Learning from University of Maryland To Explore all our courses, visit our page below. Machine Learning Courses In essence, Sentiment Analysis is the analysis of the feelings (i.e. emotions, attitudes, opinions, thoughts, etc.) behind the words by making use of Natural Language Processing (NLP) tools. If you’re not aware of what NLP tools do – it’s pretty much all in the name. Natural Language Processing essentially aims to understand and create a natural language by using essential tools and techniques. Sentiment Analysis also uses Natural Language Processing and Machine Learning to help organisations look far beyond just the number of likes/shares/comments they get on an ad campaign, blog post, released product, or anything of that nature. In this article, we’ll be talking about Sentiment Analysis in great depth. From talking about the methods and tools of Sentiment Analysis to discussing why is it so extensively used – we’ve got it all covered! In-demand Machine Learning Skills Artificial Intelligence Courses Tableau Courses NLP Courses Deep Learning Courses Learn Machine Learning online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career. Sentiment Analysis: The Math Behind It Simply reading a post will let you identify whether the author had a positive stance or a negative stance on the topic – but that’s if you’re well versed in the language. However, a computer has no concept of naturally spoken language – so, we need to break down this problem into mathematics (the language of a computer). It cannot simply deduce whether something contains joy, frustration, anger, or otherwise – without any context of what those words mean. Sentiment Analysis solves this problem by using Natural Language Processing. Basically, it recognizes the necessary keywords and phrases within a document, which eventually help the algorithm to classify the emotional state of the document. Data Scientists and programmers write applications which feeds the documents into the algorithm and stores the results in a way which is useful for clients to use and understand. Keyword spotting is one of the simplest technique and leveraged widely by Sentiment Analysis algorithms. The fed Input document is thoroughly scanned for the obvious positive and negative words like “sad”, “happy”, “disappoint”, “great”, “satisfied”, and such. There are a number of Sentiment Analysis algorithms, and each has different libraries of words and phrases which they score as positive, negative, and neutral. These libraries are often called the “bag of words” by many algorithms. Although this technique looks perfect on the surface, it has some definite shortcomings. Consider the text, “The service was horrible, but the ambiance was awesome!” Now, this sentiment is more complex than a basic algorithm can take into account – it contains both positive and negative emotions. For such cases, more advanced algorithms were devised which break the sentence on encountering the word “but” (or any contrastive conjunction). So, the result becomes “The service was horrible” AND “But the ambiance was awesome.” This sentence will now generate two or more scores (depending on the number of emotions present in the statement). These individual scores are consolidated to find out the overall score of a piece. In practice, this technique is known as Binary Sentiment Analysis. No Machine Learning algorithm can achieve a perfect accuracy of 100%, and this is no different. Due to the complexity of our natural language, most of the sentiment analysis algorithms are only 80% accurate, at best. Sentiment Analysis: Algorithms and Tools The above graphic will give you a fair idea of the classification of Sentiment Analysis algorithms. Essentially, there are two types of Machine Learning algorithms: ML-based You’re aware of the basic workings of any Machine Learning algorithms. The same route by followed in ML-based sentiment analysis algorithms as well. These algorithms require you to create a model by training the classifier with a set of example. This ideally means that you must gather a dataset with relevant examples for positive, neutral, and negative classes, extract these features from the examples and then train your algorithm based on these examples. These algorithms are essentially used for computing the polarity of a document, Lexicon-based As the name suggests, these techniques use dictionaries of words. Each word is annotated with its emotional polarity and sentiment strength. This dictionary is then matched with the document to calculate its overall polarity score of the document. These techniques usually give high precision but low recall. There is no “best” choice out of the two, your choice of method should depend solely on the problem at hand. Lexical algorithms can achieve near-perfect results, but, they require using a lexicon – something that’s not always available in all the languages. On the other hand, ML-based algorithms also deliver good results, but, they require extensive training on labeled data. The Difference between Data Science, Machine Learning and Big Data! Most Used Sentiment Analysis Tools There are many Sentiment Analysis and tracking tools available for you to use. We’ll look at five such tools that find extensive use the industry today: PeopleBrowsr PeopleBrowsr helps you find all the mentions of your industry, brand, and competitors and analyse the sentiments. It allows you to compare the number of mentions your brand had before, during, and after any ad campaigns. Meltwater Meltwater is a social media listening tool that does everything from tracking impact and sentiment analysis in real-time to understanding the competitor’s footprints. Organisations like Sodexo, TataCliq, HCL, NIIT, and many others use Meltwater to improve their online presence and impact. Google Analytics  Google Analytics helps organisations discover which channels are influencing their subscribers and customers. It helps them create reports and annotation that keeps records of all the marketing campaigns and online behaviors. HootSuite The free version of HootSuite allows the organisations to manage and measure their presence on social networks. $5.99/month will make you a premium customer that’ll entitle you to use advanced analytics features. Social Mention Socialmention is a very useful tool that allows brands to track mentions for specific keywords in blogs, microblogs, videos, bookmarks, events, comments, news, hashtags, and even audios. It also indicates if mentions are positive, negative, or neutral. How Big Data and Machine Learning are Uniting Against Cancer Sentiment Analysis: Why should it be used? With everything shifting online, Brands have started giving utmost importance to Sentiment Analysis. Honestly, it’s their only gateway to thoroughly understanding their customer-base, including their expectations from the brand. Social Media listening can help organisations from any domain understand the grievances and concerns of their customers – which eventually helps the organisations scale up their services. Sentiment Analysis helps brands tackle the exact problems or concerns of their customers. According to some researchers, Sentiment Analysis of Twitter data can help in the prediction of stock market movements. Researchs show that news articles and social media can hugely influence the stock market. News with overall positive sentiment has been observed to relate to a large increase in price albeit for a short period of time. On the other hand, negative news is seen to be linked to a decrease in price – but with more prolonged effects. Ideally, sentiment analysis can be put to use by any brand looking to: Target specific individuals to improve their services. Track customer sentiment and emotions over time. Determine which customer segment feels more strongly about your brand. Track the changes in user behavior corresponding to the changes in your product. Find out your key promoters and detractors. Clearly, sentiment analysis gives an organisation the much-needed insights on their customers. Organisations can now adjust their marketing strategies depending on how the customers are responding to it. Sentiment Analysis also helps organisations measure the ROI of their marketing campaigns and improve their customer service. Since sentiment analysis gives the organisations a sneak peek into their customer’s emotions, they can be aware of any crisis that’s to come well in time – and manage it accordingly. Popular AI and ML Blogs & Free Courses IoT: History, Present & Future Machine Learning Tutorial: Learn ML What is Algorithm? Simple & Easy Robotics Engineer Salary in India : All Roles A Day in the Life of a Machine Learning Engineer: What do they do? What is IoT (Internet of Things) Permutation vs Combination: Difference between Permutation and Combination Top 7 Trends in Artificial Intelligence & Machine Learning Machine Learning with R: Everything You Need to Know AI & ML Free Courses Introduction to NLP Fundamentals of Deep Learning of Neural Networks Linear Regression: Step by Step Guide Artificial Intelligence in the Real World Introduction to Tableau Case Study using Python, SQL and Tableau In Conclusion… More or less every major brand these days relies heavily on social media listening to improve the overall customer experience. If you’re one of the interested souls and want to explore this topic in further depth, we recommend you go through the various kinds of algorithms (the ones we displayed in a graphic earlier) and implementations of Sentiment Analysis in more detail. Also, If you’re interested to learn more about Machine learning, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.
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by Amandeep Rathee

21 Feb'18