🧩 Phase 0 — Prerequisites

Build your mathematical and programming foundation for Reinforcement Learning.

Topics

• Probability, Statistics, Linear Algebra, Calculus
  → Start with Probability Basics ▸
  → Continue with Linear Algebra & Matrix Calculus ▸
• Gradient Descent and Optimization
  → Explore Gradient Descent & Optimization ▸
• Python Essentials (scikit-learn, PyTorch, Gymnasium)
  → Start with scikit-learn Basics ▸
  → Continue with PyTorch Basics ▸
  → Explore Gymnasium Basics ▸
• Basic Machine Learning (Regression, Classification, Evaluation Metrics)
  → Learn with Basic Machine Learning ▸
• Basic Deep Learning (Neuron, MLP, CNN)
  → Learn with Basic Deep Learning ▸
• Introduction to Markov Chains
  → Start with Introduction to Markov Chains ▸

Mini Projects

• End-to-End Linear Regression
  → Try it here: Linear Regression Project ▸
• Image Classification using CNN
  → Explore here: CNN Image Classification ▸

📁 Source folder:
00-Prerequisites