Welcome to the Essential Mathematics for Machine Learning course. A structured math learning path for ML, deep learning (DL), and generative AI (GenAI) builds progressively from beginner foundations to advanced topics like tensors and information theory.

This expanded outline adds modules on multivariable extensions, discrete math, and DL-specific concepts (e.g., automatic differentiation, convolutions) while keeping it beginner-friendly with visuals, code, and ML tie-ins.

---

Course Structure

Part 1: Foundations

• Module 01: Linear Algebra Basics
• Module 02: Calculus Fundamentals

Part 2: Probability & Optimization

• Module 03: Probability & Statistics Essentials
• Module 04: Advanced Optimization

Part 3: Deep Learning Math

• Module 05: Advanced Linear Algebra & Multivariable
• Module 06: Discrete Math & Info Theory for DL/GenAI

---

Path Overview

12-16 weeks total, 6 modules (25 chapters). Weekly: 2-3 chapters, quizzes, Python labs (NumPy, SymPy). Leads directly to ML courses (e.g., regression, SVMs), DL (CNNs, RNNs), GenAI (diffusion models, transformers).

Next Steps Integration

Post-path: Apply in scikit-learn (ML), PyTorch (DL/GenAI). Include projects like MNIST classification or simple GAN. This covers 90% of math needs up to cutting-edge.