Month 2: Machine Learning Fundamentals

Week 1: Introduction to Machine Learning

• Recap of Machine Learning basics
• The machine learning process
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Applications of Machine Learning

Week 2: Supervised Learning Techniques

• Regression Analysis
• Decision Trees
• Random Forests
• Gradient Boosting
• Support Vector Machines

Week 3: Unsupervised Learning Techniques

• Clustering
• Dimensionality Reduction
• Principal Component Analysis (PCA)
• Independent Component Analysis (ICA)
• t-SNE

Week 4: Reinforcement Learning Techniques

• Introduction to Reinforcement Learning
• Markov Decision Processes (MDPs)
• Policy and Value Iteration
• Q-Learning
• Deep Reinforcement Learning