In the introduction part of the lectures it covers basic definitions of machine learning.
The lectures also gave a high level how the lectures are separated as
- Supervised Learning
- UnSupervised Learning
- Reinforcement learning
ML where we have sample input and output based on which our algorithm or system performs and find out the exact output for the testing data
Here the algorithm will learn by itself based on the concepts. Example how we are able to identify the horses since from childhood we learnt on various attributes of the horses