Introduction
What is Machine Learning, Unsupervised Learning, Reinforcement Learning
Machine Learning Use-Cases, Machine Learning Process Flow, Machine
Learning Categories, Linear regression and Gradient descent.

Supervised Learning
Classification and its use cases, Decision Tree, Algorithm for Decision Tree
Induction
Creating a Perfect Decision Tree, Confusion Matrix, Random Forest. What is
Naïve Bayes, How Naïve Bayes works, Implementing Naïve Bayes Classifier,
Support Vector Machine, Illustration how Support Vector Machine works,
Hyper parameter Optimization, Grid Search Vs Random Search,
Implementation of Support Vector Machine for Classification.

Clustering
What is Clustering & its Use Cases, K-means Clustering, How does K-means
algorithm work, C-means Clustering, Hierarchical Clustering, How
Hierarchical Clustering works.

Why Reinforcement Learning, Elements of Reinforcement Learning,
Exploration vs Exploitation dilemma, Epsilon Greedy Algorithm, Markov
Decision Process (MDP)
Q values and V values, Q – Learning, α values.