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# 15 Best MATLAB Project Ideas & Topics For Beginners [2024]

Updated on 10 July, 2024

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Diving into the world of engineering and data science, I’ve discovered the potential of MATLAB as an indispensable tool. It has accelerated my career and ignited my passion for innovative problem-solving. In this article, MATLAB project ideas for beginners, I aim to share my journey and expertise with those embarking on their path in this dynamic field. My intention is to provide a foundational guide for aspiring professionals eager to delve into technical computing and algorithm development.

Through a detailed exploration of MATLAB fundamentals, the acquisition of critical skills via project work, and an understanding of the importance of MATLAB projects for professional development, I hope to simplify the path to mastering MATLAB. This article is designed to make MATLAB approachable and prove its value as a cornerstone in your arsenal of professional tools.

We have MATLAB projects for beginners a gentle approach of multiple skill levels. Whether you’re a beginner or an expert, you’d find a brain-teasing project here.

## What is MATLAB?

“Matrix Laboratory” is the full name of MATLAB. It is a high-performance programming language for technical computing that combines programming, calculation, and visualization in a user-friendly environment.

MATLAB is a programming platform for scientists and engineers. It uses the MATLAB language, combining matrix and array mathematics with design processes and iterative analysis. By using MATLAB, you can create algorithms, analyze data, build models, and apply them. MATLAB’s apps, built-in functions, and language allow you to use different methods to solve a particular problem. MATLAB finds applications in many areas, including control systems, communications, machine learning, computational biology, and deep learning.

## Top MATLAB Project Ideas with source code

The following are some of the most exciting MATLAB projects with source code so that you can test your skills. Let’s get started:

### 1. Build a Car Parking Indicator

Parking a car can be tricky. It requires precision and a lot of practice. You can use MATLAB to make things easier for the driver, however, by building a car parking indicator. You can take inspiration from various parking indicator systems.

An automated car parking indicator would alert the driver when the car is too close to an object. This way, the driver can avoid those objects and turn the vehicle accordingly. You can build a car parking indicator for private parking spaces or open spaces. Such a system can have many benefits:

• The driver would save time and park his/her car more efficiently.
• Parking spaces would also be used more efficiently.
• The chances of a vehicle getting damaged would decrease drastically.

Your system can guide the driver to a nearby suitable parking space. You can take it a step further and add the functionality of suggesting a parking space only if it’s available. Maybe your system can determine if a car park has open slots or not, and it can indicate a parking space to the driver of the vehicle accordingly. The sensors can co-ordinate and help in guiding the driver to an open and nearby parking slot.

Source Code: Car Parking Indicator

### 2. Use Artificial Neural Network for Image Encryption

Privacy issues have become highly prevalent in recent years. This is one of the best MATLAB project ideas for mechanical engineering for you on this list if you take an interest in cybersecurity and cryptography. You can perform image encryption by taking the help of Artificial Neural Networks (ANNs in short).

Image encryption can prevent unauthorized parties from viewing and accessing images. This way, your data can remain safe. In simple terms, image encryption hides its information. In image encryption, you convert the original plaintext into ciphertext (which can seem like a bunch of nonsense). You can save and transmit this ciphertext over your network, and at the receiver’s end, the ciphertext would convert into the original plaintext.

Neural Networks are machines that behave similarly to how a human brain functions. You can encrypt images on the sender’s end through one ANN and use another ANN to decrypt the image on the receiver’s end. You can use MATLAB to build a complete image encryption system that uses Artificial Neural Networks. After completing this project, you’d be familiar with cryptography as well.

Source Code: Image Encryption

### 3. Design and Apply an Electronic Differential System

An Electronic Differential System allows vehicles to balance them better while turning or running on curved paths. Automotive manufacturers use this system in place of the mechanical differential. This system provides every driving wheel with the required torque and enables multiple wheel speeds.

In a curved path, the vehicle’s inner and outer wheels would have different rotation speeds as the inner wheels would require a smaller radius. An Electronic Differential System uses the motor speed signals and steering wheel command signal to determine the required power for every wheel, so they get the necessary torque.

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It’s an advanced technology that offers many advantages, which its mechanical counterpart fails in providing. For example, the electronic differential is lighter than mechanical differential in terms of weight. The wheel with the least traction wouldn’t limit the torque as it would with a mechanic differential. These systems respond faster and offer many functionalities unavailable in the other one, such as traction control.

You can use ml projects for final year to design and implement an electronic differential system. You’ll need to create an embedded system design as well for better application.

Source Code: Electronic Differential System

### 4. Build a MATLAB Based Inspection System with Image Processing

In this project, you’ll build a MATLAB-based inspection system. Machine vision is becoming an accessible technology in the manufacturing industry because of its versatility. And one of the most significant areas where machine vision can find use is in the inspection stage of product development. Quality inspection is necessary to make sure the product doesn’t have any defects.

You can use MATLAB to create an automated inspection system, and you’ll have to employ image processing. With machine vision image processing, you can perform multiple tasks at once:

• Counting the number of dark and light pixels
• Discovering blobs of joined pixels in an image
• Segmenting a part of an image or change the representation
• Recognizing patterns in an image by matching templates
• Reading barcode and 2D code.

You can perform many other tasks with machine vision. Your automated inspection system would have to determine whether to accept the final product or reject it. It will make the manufacturing process far more efficient and effective.

Source Code: Inspection System with Image Processing

### 5. Perform Image Encryption and Verification with Chaotic Maps

The project is a little different from the one we’ve discussed previously. In this project, you’ll use chaotic maps to encrypt images on the block and steam levels. There is n number of chaotic maps present that generate keys for encryption, so there would be n number of equations involved. Every equation can have n number of constants.

All of these constants would have specific values (random numbers). You can use a neural network to produce a particular series of numbers for image encryption. For image authentication, you’d have to create a simple algorithm to ensure that the sender and receivers are the right people.

Chaotic maps would make the encryption secure through substituting the image with the cover image and encrypting the former n times. Such secure encryption would ensure that your end product remains free from brute force attacks and differential attacks.

Source Code: Image Encryption Using Chaotic Map

Also try: Python Project Ideas and Topics

### 6. Measure an Object’s Diameter in an Image by using MATLAB

Computer vision is a prominent field of study. It finds applications in many areas due to its unique utility. You can use MATLAB to measure an object’s diameter in an image.

This application can find uses in many areas where you can’t find the diameter of an object physically. For example, suppose you need to measure the size of a building. In this case, the physical measurement would be nearly impossible, so you’ll need to use computer vision.

Your MATLAB script should first import the image, separate the required object from the background, and in the end, use MATLAB functions to find the object’s diameter. While this project might seem quite simple, it will help you showcase your image processing skills while also highlighting your knowledge of multiple MATLAB functions.

Source Code: Object’s Diameter in an Image Using MATLAB

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### 7. Use MATLAB to Automate Certificate Generation

This project is also among the beginner-level MATLAB project ideas for students. In this project, you’ll create an automated certificate generator using MATLAB. Many institutions certify companies based on their performance and achievements. Educational institutions also generate report cards and certificates for their students. You can create an automated certificate generator, which will make this process efficient and straightforward.

This project idea might seem too simple, but you can make it complicated by adding the functionality of generating detailed reports for large datasets.

Source Code: Automate Certificate Generation

### 8. Create Light Animations with MATLAB and Arduino

This is one of the beginner level MATLAB projects on our list. In this project, you’ll use MATLAB and Arduino to create a graphical user interface to control the lighting patterns of multiple lights. By controlling their lighting pattern, you can create various light animations. Using a GUI will allow you to perform many other tasks while running the animation.

We recommend using Arduino Uno for this project. It’d be the hardware of this project, and the software would be the Arduino IDE. You can connect the Arduino Uno board with the required lights. After you’ve connected Arduino Uno with MATLAB, you’ll be able to create simple light animations with the same.

It’s an easy project, but it’ll surely help you explore real-life MATLAB applications and help you realize its versatility. After you’ve made simple light animations, you can take this project a step further and add more lights to create more complex animations.

Source Code: Create Light Animations

### 9. Log Sensor Data in MS Excel

This project requires you to use Arduino Uno with MATLAB to log sensor data in MS Excel. You can add LM35 (a temperature sensor) to your Arduino interface, which would connect to MATLAB through ArduinoIO.

Once you’ve connected Arduino with MATLAB, you’ll need to create a program that transmits the sensor’s data into an Excel sheet. You’ll need to have MS Excel installed on your PC to complete this project. Once you’ve finished this project, you’d have a graphic user interface that allows you to see the logs of the sensor data.

To take it a step further, you can add more sensors and log their data into the same excel file (or in multiple different files). This project will give you plenty of experience in using GUI with MATLAB.

Source Code: Log Sensor Data in MS Excel

### 10. Simulate an Artificial Neural Network

Artificial Neural Networks are machines that imitate the functioning of a human brain. Their purpose is to mimic the behavior of a mind and act accordingly. In this project, you can simulate an ANN by creating models and training them.

Before you work on this project, you should be familiar with the basic concepts of artificial intelligence and machine learning. You’ll first need to create a data model that takes particular input and generates a particular output. First, you’ll need to train the model by giving it a list of inputs and outputs. Once you’ve prepared the model, you’d give the model a data list with no outputs.

After completing this project, you’d be familiar with artificial intelligence, machine learning, and relevant technologies.

Source Code: Simulate an Artificial Neural Network

### 11. Analyze and Design an Antenna

While everything is becoming wireless, their connectivity relies largely on antennas. An antenna’s design can have a significant impact on its connection, power consumption, and data retention capabilities. The design should make the antenna compact while allowing it to have a substantial beam width to perform information transmission without any loss.

It’s an excellent project for anyone interested in electronics and communications. You should be familiar with the workings of antennas before you work on this project, however. For example, you should know about the ideal antenna pattern and how a real antenna works.

You should also be familiar with the Yagi-Uda antenna, which is the most common TV antenna you see on rooftops. You can estimate (approximately) the operating frequency of such an antenna by viewing its length. You can build a MATLAB program that can perform such estimation with high accuracy and give you the required results.

Source Code: Analyze and Design an Antenna

### 12. Build a Circuit Design Calculator

To build a circuit, you must calculate the component values by using the circuit theory and its formulae. Circuit theory is among the oldest and essential branches of electrical engineering. And its calculations take a lot of time and effort. You can create a MATLAB program that can perform those calculations and help an engineer design a better circuit. Not only will such a system save the user a lot of time, but it will also enhance the accuracy of circuit analysis by minimizing human error.

Your program can analyze and figure out circuit design with inductors, transistors, diodes, capacitors, and other critical components. The program can design highly complex circuits and solve problems accordingly.

Source Code: Circuit Design Calculator

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### 13. Compress Images without Loss

Modern cameras have become capable of taking highly detailed images. But an increase in an image’s level of detail also leads to a rise in its size. That’s why image compression technologies have become prevalent. You can use MATLAB to perform image compression as well.

In this project, you would aim to compress an image without compromising its quality. In other words, you’ll have to perform lossless image compression. To do so, you can use the discrete cosine transform algorithm. To find out how much loss took place while compressing the image, you can derive the mean-square error (also known as MSE) of your process. To implement these algorithms in MATLAB, you’ll have to use the required functions.

Source Code: Compress Images without Loss

Also Read: Machine Learning Project Ideas

### 14. Perform Real-Time Face Detection with MATLAB

Face detection can find applications in many areas. You can use face detection capabilities for image enhancement, security, as well as surveillance. While it’s quite natural for us humans to detect faces, we can’t say the same about computers. A simple change in lighting can cause various intra-class variations, that’s why it’s a complicated issue for machines.

You can build a MATLAB-based face detection system, and you can use the Viola-Jones algorithm. There are many other facial recognition algorithms, but we have chosen the viola-jones algorithm for this project.

It first creates a detector object, then takes the primary image, finds the necessary features, and annotates them. This project will give you experience working with facial recognition technology, which has gained popularity in many fields.

Source Code: Real-Time Face Detection with MATLAB

### 15. Build Laser Guidance for a Vehicle

In this project, you’d develop a program that can use lasers to inform the vehicle of upcoming road conditions. This technology can be really helpful for harsh terrains (such as snowy roads, dirt roads, etc.). You’d need to develop an algorithm in MATLAB that converts the scan sequences into readable data so the user can see what kind of terrain is up ahead. This way, the driver can prepare him or herself accordingly and drive safely. An autonomous vehicle can use this technology, as well.

This project will help you get familiar with the application of MATLAB in automotive engineering. It’ll also help you understand how autonomous vehicles work. You can learn more about this project here

## What are the Skills That You Will Acquire Through MATLAB Projects?

Engaging in MATLAB for beginners projects offers a diverse range of skills that are valuable across various industries and fields of study. MATLAB, a powerful programming and numerical computing platform, enables individuals to tackle complex problems, conduct data analysis, and develop innovative solutions. Here are some skills you can acquire through MATLAB project ideas:

### 1. Programming Proficiency

MATLAB simulation projects involve writing code, which helps you develop strong programming skills. You’ll learn about variables, data structures, loops, and conditional statements, which are fundamental concepts in programming.

### 2. Data Analysis and Visualization

It helps in excels in data analysis and visualization. Through projects, you’ll gain expertise in importing, processing, and visualizing data, which is crucial in fields like data science, finance, and engineering.

### 3. Algorithm Development

It allows individual to develop and implement algorithms efficiently. On top of that, you’ll also learn about designing and optimizing algorithms for tasks like, image processing, signal processing, and machine learning.

### 4. Mathematical Modeling

ML is widely used for mathematical modeling and simulations. You’ll acquire skills in creating mathematical models of real-world phenomena and simulating their behavior.

### 5. Image and Signal Processing

MATLAB is renowned for its capabilities in image and signal processing. You’ll learn how to enhance images, analyze signals, and extract meaningful information from them.

### 6. Machine Learning

It offers extensive tools and libraries for machine learning. Through projects, you can develop skills in building and training machine learning models for tasks like classification, regression, and clustering.

### 7. Numerical Optimization

MATLAB is ideal for solving optimization problems. You’ll gain experience in formulating and solving optimization problems, which are valuable in engineering and operations research.

### 8. Simulink

Simulink, a MATLAB toolbox, is used for modeling and simulating dynamic systems. You can acquire skills in system modeling and control design, which are essential in fields like robotics and control engineering.

### 9. Parallel and Distributed Computing

MATLAB allows you to leverage parallel and distributed computing resources. Learning to distribute your computations efficiently is valuable for handling large datasets and complex simulations.

### 10. Problem-Solving Skills

The projects often involve tackling real-world problems. You’ll develop problem-solving skills by breaking down complex challenges into manageable tasks and finding creative solutions.

### 11. Collaboration and Documentation

Working on projects in MATLAB encourages collaboration and the documentation of your code and findings, which are essential skills for teamwork and knowledge sharing.

### 12. Project Management

Managing and completing MATLAB projects requires organizational skills, time management, and goal setting, which are transferable to various professional settings.

## Why Opt for MATLAB Projects?

Engaging in MATLAB project ideas offers several compelling reasons:

### 1. Practical Application

MATLAB is a versatile platform used in academia and industry for solving real-world issues. Through projects, you can apply theoretical knowledge to practical scenarios, enhancing your understanding and skills.

### 2. Skill Development

MATLAB projects cultivate a wide range of skills, including programming, data analysis, and mathematical modeling, which are highly transferable and sought after in many professions.

### 3. Interdisciplinary Applications

MATLAB is not limited to a specific field; it’s used in diverse domains such as engineering, finance, biology, and physics. This versatility allows you to explore various areas of interest and adapt your skills to different contexts.

### 4. Research Opportunities

MATLAB is a common tool in research. Engaging in MATLAB projects can open doors to research collaborations, enabling you to contribute to cutting-edge advancements in your field of study.

### 5. Career Advancement

Proficiency in MATLAB can be a valuable asset on your resume, making you more attractive to employers in technical and scientific fields.

### 6. Problem-Solving

MATLAB projects often involve complex problem-solving, honing your ability to analyze challenges, devise solutions, and make informed decisions.

### 7. Portfolio Building

Completing MATLAB projects creates a portfolio showcasing your practical skills and problem-solving abilities, which can impress potential employers or academic institutions.

### 8. Personal Growth

Working on projects in MATLAB fosters perseverance, creativity, and self-confidence as you overcome obstacles and see tangible results.

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## Learn More About MATLAB

Exploring MATLAB Project Ideas for beginners equips you with a practical understanding of MATLAB and significantly enhances your analytical and computational skills. Delving into projects ranging from simple calculations to complex data analysis and visualization offers an invaluable hands-on experience in today’s data-driven world. Whether you‘re a student stepping into programming and engineering or a professional seeking to refine your skills, MATLAB projects offer a versatile platform for learning and innovation.

This guide aims to inspire and equip beginners with a diverse range of project ideas, showcasing the potential of MATLAB in solving real-world problems. As you embark on this journey, remember that each project is a step towards mastering a tool that is indispensable in engineering and science.

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## Frequently Asked Questions (FAQs)

### 1. What is a MATLAB code?

MathWorks developed MATLAB, a multi-paradigm computer program and numeric computing environment. Matrix operations, function and data visualization, algorithm implementation, user interface building, and interfacing with programs written in other languages are all possible with MATLAB. Although MATLAB is primarily designed for numerical computations, an optional toolbox employs the MuPAD symbolic engine to provide symbolic computing capabilities. Simulink, a separate software, brings graphical inter simulation & model-based design for embedded systems to the mix.

### 2. Where is MATLAB used?

MATLAB is a computer language that engineers and scientists use to study and build systems and products that change the world. The MATLAB language, a matrix-based language that allows the most natural expression of mathematics and computer science, is at the heart of MATLAB. In industry and academia, MATLAB is used by millions of engineers and scientists for a variety of applications, including machine learning and artificial intelligence, remote sensing and communication systems, computer vision, control mechanisms, instrumentation, computational finance, and biomedical engineering.

### 3. How many types of operators are in MATLAB?

A symbol called an operator instructs the compiler to do different numerical or logical operations. MATLAB is primarily designed to work with complete matrices and arrays. As a result, MATLAB functions may work with both scalar as well as non-scalar data. To cope with variables, functions, and mathematical operations, MATLAB has a variety of operators, symbols, as well as special characters. Simple mathematical calculations like add, subtract, multiply, division, and power are made easier by arithmetic operators. Value comparison procedures are performed via relational operators. Logical operators carry out logical functions and give an output as true or false in the Boolean state using the digits 1 and 0.

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Pavan Vadapalli

Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.

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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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02 Nov'17

6.14K+

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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30 Jan'18

8.3K+

A Beginner&#8217;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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30 Jan'18

10.99K+

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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06 Feb'18

20.1K+

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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06 Feb'18

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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. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.
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14 Feb'18

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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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21 Feb'18

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