Machine Learning Nowadays, large amount of structured and unstructured data is available. This repository is to understand how ML algorithms work. Supervised Learning: Classification vs Regression Classification: supervised learning task with discrete class labels Goal: Predict class labels of new instances, based on past observations. Binary classification vs Multiclass classification Regression: Prediction of continuous outcome Goal: Fit a line to it minimizing the distance between sample points and the fitted line Reinforcement Learning The system (aka agent) improves its performance based on interactions with an environment. Trial-and Error approach The agent receives feedback (reward) from the environment. This reward is not the correct ground truth. It is a sample experience. Extensive interaction with the environment allows agent to learn a series of actions that maximizes this reward.