Machine learning and its types

What is Machine Learning?

Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.

We can think of machine learning as approach to automate tasks like predictions or modelling

Example -

·        an email spam filter system

·        movie recommendation systems

The nice and powerful thing about machine learning is:

It learns when it gets more data and hence it gets more and more powerful the more data we give them.

Types of Machine Learning

·        supervised,

·        unsupervised and

·        reinforcement learning

Supervised Learning

The goal of supervised learning is to learn a model from labelled training data that allows us to make predictions about future data.

For supervised machine learning to work we need to feed the algorithm two things:

1.      the input data and

2.      our knowledge about it labels

Example -:

·        an email spam filter system - Input data (emails) Labels (spam or not spam)

Classification -: It is used to predict categories or class labels based on past observations.

Regression: It is used to predict a continuous outcome.

Unsupervised Learning

The goal of unsupervised learning is to discover hidden structure or patterns in unlabeled data and it can be divided into two subcategories.

Clustering -: It is used to organize information into meaningful clusters (subgroups) without having prior knowledge of their meaning.

Example -: Application in search engine

                        - Structuring search result

                        - Suggesting related pages

Dimensionality Reduction (Compression) -: It is used to reduce a higher dimension data into lower dimension ones.

Example -: A telescope has terabytes of data and not all of these data can be stored and so we can use dimensionality reduction to extract the most informative features of these data to be stored.

Reinforcement Learning

The goal of reinforcement learning is to develop a system that improves its performance based on the interaction with a dynamic environment and there is a delayed feedback that acts as a reward.

Example -: Chess game, the computer decided a series of moves and the reward is the “win” or “lose” at the end the game.