Machine learning algorithms are algorithms that learn (often predictive) models from data. I.e., instead of formulating “rules” manually, a machine learning algorithm will learn the model for you.
in another word, Machine learning is a technique to achieve AI through algorithms trained with data.
There are three ways in which machines learn:
Supervised Learning:
Supervised learning is a method in which the machine learns using labelled data. It is like learning under the guidance of a teacher. The training dataset is like a teacher which is used to train the machine. Model is trained on a pre-defined dataset before it starts making decisions when given new data
Unsupervised Learning:
Unsupervised learning is a method in which the machine is trained on unlabelled data or without any guidance, It is like learning without a teacher. The model learns through observation & finds structures in data. Model is given a dataset and is left to automatically find patterns and relationships in that dataset by creating clusters.
Reinforcement Learning:
Reinforcement learning involves an agent that interacts with its environment by producing actions & discovers errors or rewards. It is like being stuck in an isolated island, where you must explore the environment and learn how to live and adapt to the living conditions on your own. The model learns through the hit and trial method. It learns on the basis of reward or penalty given for every action it performs
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