Introduction to Machine Learning

Hello Shouters !! Today we will have a look at the practical approach to machine learning.

Have u ever wondered how Netflix recommends us the exact shows we like? Do you ever been amazed by how amazon suggests exactly the products we need? Have you ever thought how Alexa is understanding human voice after all it is a machine or how uber calculates the time of arrival of a cab?

The ease these products make in our lives has kept us away from these questions but these questions have an answer which is very popular these days as the name of MACHINE LEARNING.

Machine learning is the state-of-the-art technology at this hour of the time. Every industry has accepted the dominance of machine learning in making the growth of the industry fast.

As of the B.Tech students, it is a great time to understand machine learning and help the industry to grow at a rate faster than ever as well as draw a good amount of salaries from the employer as machine learning engineer or data scientists are given very high scale packages up to 6-10lac to even freshers and this amount grows exponentially with the experience.

In this blog, we will discuss the basic theory of machine learning and will keep it brief so that we can jump to practical asap as only the practical application will make you better at machine learning

MACHINE LEARNING is basically an application of ARTIFICIAL INTELLIGENCE that provides the system to learn and improve automatically from the experience without explicitly being programmed. So in machine learning, we don’t need to code everything, the machine will give the answers to our queries automatically. As we can see in the above examples the Netflix recommendations are automatic for every user and similarly, amazon recommends products automatically.

Types of machine learning:

1)Supervised Learning-In this type, labels(output) are given. Here, we know the output of the several used cases and predict the output of some new cases based on these used case outputs.

2)Unsupervised Learning-In this type, labels(output) are not given. Here, we don’t know the output of any used case but we still try to get some information from these used cases through some techniques which we will discuss later in the upcoming blogs.

3)Reinforcement learning-which learns from its mistakes. Here, no used case is given. The machine will give the answers and will be externally told that whether it is a right or wrong answer and will learn according to this experience of its own. We hope to know you will know what is the practical approach to machine learning.

Don’t worry if you don’t understand the types of machine learning here, these would be better understood in a practical approach which we will follow in our next blogs.

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Happy learnings!!!!!

Frequently Asked Questions-

How machine learning and artificial intelligence-related?

Machine learning is a subset of artificial intelligence

Are there any prerequisites for learning machine learning?

Python or R language knowledge is required.

What is the scope of Machine Learning?

The scope is very broad as it is required for automation of machine and every industry needs automation.

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