We often describe intelligence as the ability to work efficiently or to solve problems. However, this concept of adopting intelligence is now changing in the IT world – it is leading to the development of artificial intelligence (AI) and ushering in the fourth industrial revolution.
The impact of AI in society is transformative galvanizing in the areas of finance, transportation, medical research, space exploration and meteorology hanging – it is driving the development of artificial intelligence (AI) and bringing about a fourth industrial revolution.
ML and AI
Artificial Intelligence, in short, AI, is a field of study in automation industries. Conceptually, AI adopts technological means to develop intelligent machines. And machine learning, ML, is one of the ways of executing the concept of AI.
Machine Learning is a branch of Artificial Intelligence and is a vast field of study. It inherits the principle from artificial intelligence aimed at training machines. ML deals with developing computer algorithms that let the computer programs automatically improve machine intelligence through experience.
ML field focuses on synthesising meaningful concepts, making them practically implementable from historical data. It involves a mechanism of automatic and periodic learning by acquiring skills, knowledge and deriving the right decisions from a series of experiences. However, its learning scope could be the overall field of study or specific techniques that address the objective.
With strong roots in statistics, Machine Learning is becoming one of the most interesting and fast-paced computer science fields to work in. As a subject of study, Machine Learning mainly focuses on different algorithms, their working based on mathematics, and implementing the algorithms in a programming language.
Unlike traditional programming, ML development need not be explicitly programmed. The algorithms train the programs (machines) to behave smartly. Machine learning thus allows us to determine patterns and develop models for tasks that are hard for humans to handle.
Machine learning is being applied to monotonous as well as complex logic-based processes. The implementation of ML in the industry enhances performance in more efficient and intelligent ways. The application of ML in the industries is limitless.
For example, some of the everyday life tasks performed via the web, say, chatbots, image recognition, ad serving, search engines, fraud detection, spam filtering, etc., work on machine learning models.
Industry Adoption of AI
Digital evolution has boosted the adoption of AI in the technology industry. Besides big players like Amazon and Google, even smaller startups are focusing on AI-focused development in their business. The adoption of ML algorithms primarily to improve the customer experience has resulted in a magic transition in the market.
Evolution of AI
In 1935, British computer pioneer, Alan Turing, described a machine with unlimited memory and scanners that went through these memories, symbol by symbol, reading and writing more symbols, which would be indicated by the instructions stored in memory as the scanner symbol. This is the Turing machine, which is the foundation of modern computer systems.
Since then AI has developed rapidly. In 1945, Turing predicted computers would play excellent chess.
By 1977, Deep Blue, a chess program, beat the world champion, Garry Kasparov.
Uses of ML
ML has omnipresence in the industry. It is widely used in various sectors, including IT-based production, research, medical, marketing, and so on.
ML is now used in major financial analysis and decisions, including share price prediction, electronic trading, loan risk assessments, real estate valuation, etc.
AI is also greatly used in telecom, satellite, and GPS. It is vital in space explorations, including the ongoing NASA Mars Perseverance Probe.
In the medicine domain, it is used to detect heart and lung ailments, and also treat cancers.
In agriculture, it is being used to predict the most efficient harvest season. It also has a presence in automobile manufacturing, and in market research businesses to tackle targeted marketing and adoption of online searches in several other sectors.
Machine visual perception is used in surveillance and tracking. Some courts in the US now use the algorithms of ML models to decide the chances of defenders becoming repeat offenders.
The ML technology is also used to make deepfakes, now experiential on humour ground, however, over time, it may cause a threat, especially like fake news.
Market Demand for AI
According to the Gartner Report of 2021, by 2025, 50% of large enterprise IT leaders will need Operations Technology Management (OTM) skills to support artificial intelligence (AI) and enhanced intelligence.
As per IDC, the figures in forecast growth for the global AI market will be up by 16.4% year over year in 2021 to $327.5 billion. Also, by 2024, the market is expected to break the $500 billion mark with a five-year compound annual growth rate (CAGR) of 17.5% and total revenues reaching an impressive $554.3 billion.
In the Indian context, the IDC report cited the growth of artificial intelligence spending by over 30%. The AI spending is likely to grow from $300.7 million in 2019 to $880.5 million in 2023 at a CAGR of 30.8 %.
Salary in AI
As per PayScale, the average salary for professionals in Artificial Intelligence (AI) is Rs1,546,314 and for ML engineers, ₹800k. The average machine learning salary in India is approximately Rs. 6,86,281 per year, inclusive of incentives.
It has been found that an AI Engineer gets a lucrative hike of up to 60–80% when switching jobs, whereas the other stream professional could bag an average of 20–30%.
Professionals in AI can have one of the roles in the following title:
- Big Data Engineer
- Business Intelligence Developer
- Data Scientist
- Machine Learning Engineer
- Research Scientist
- AI Data Analyst
- AI Engineer
- Robotics Scientist, etc.
Who can Become an ML Engineer?
A math-savvy student with a flair in coding is the most desirable candidate to choose a profession in the AI arena. Graduates with mathematics and/or statistics background may opt for becoming ML engineers. A minimum Bachelor’s or Master’s degree preferably in mathematics or statistics, if not computer science, data science, software engineering is required. Having hands-on expertise in mathematics-based programming languages, such as Python, R, or equivalent is a plus point in ML.
- The knowledge of statistics and probability principles set the foundation of many ML algorithms.
- Besides numerical concepts, having fundamental concepts of software engineering clear would ease the implementation.
- Inclination towards working with different ML algorithms and libraries is essential.
- Get the knowledge of data modelling and evaluation methods that would help practice sample ML projects.
- There are a lot of online avenues to participate in online coding forums and learn more about ML fundamentals.
In addition to having ML skills and the capacity of managing AI-based projects, industries look for certifications in ML/AI courses. Therefore, get enrolled in an official course that fits you. The majority of online courses are available to opt for.
One of the reputed institutions named upGrad would be to your rescue. You can benefit from the courses upGrad offers. Choose one of the online courses in AI and ML and be a professional ML Engineer after joining online and see achieve your dream.
Over the decades of successful transition into e-learning, several online channels ease students to enrol in the desired course. There are several providers that offer such courses to help professionals acquire credentials in their field of study. A brand named upGrad is one such pioneer provider of technical and business-related online courses, including AI and ML.
Courses offered by upGrad
There are four major courses in Machine Learning available at upGrad.
- Advanced Certificate in Machine Learning and Deep Learning – Become an ML Engineer by learning how to build a chatbot, a news recommendation engine, and lots more
- Advanced Certificate in Machine Learning and NLP
- Executive PG Program in Machine Learning and AI – Become a Machine Learning Engineer and learn how to train an agent to play tic tac toe, train a Chatbot, and lots more
- Master of Science in Machine Learning and AI – Pursue an integrated Master’s Program in Machine Learning and AI from IIIT-B and LJMU. It is 10 times more economical than offline programs.
All courses are online and are designed for working professionals.
The Eligibility criteria benchmarked as a min Bachelor’s Degree with 50% or equivalent passing marks. Students having a minimum of 1 year of work experience or a degree in Mathematics or Statistics are more suitable.
Why choose upGrad Courses?
The courses are approved by WES (World Education Services) and accredited with IIT Bangalore, a deemed university by UGC, AICTE approved. As per NIRF Rankings, the institute stands in the top 70 Engineering Universities.
The curriculum is designed by the best-in-class experts and leading faculty members. The content includes multimedia, videos, case studies, and projects.
Now that you have a fair idea of the importance of AI and ML, you can decide on studying machine learning. Get information on where to learn machine learning, how to start learning machine learning, as well as the best way to learn machine learning.
The course provider institution, upGrad provides a Executive PG Program in Machine Learning and AI and a Master of Science in Machine Learning & AI that may guide you toward building a career. These courses will explain the need for Machine Learning and further steps to gather knowledge in this domain covering varied concepts ranging from Gradient Descent to Machine Learning.