Welcome to the Machine Learning Mathematics course! 🧠 This path focuses on building Python projects that apply mathematical concepts critical to machine learning. Whether you're diving into linear algebra, probability, or optimization, this course will help you connect theory with practical coding.
📘 Course Content Overview
Linear Algebra Foundations
- Vectors & Matrices
- Eigenvalues and Eigenvectors
- Matrix Decomposition (e.g., SVD)
- 📌 Example Project: Implement PCA using NumPy
Probability & Statistics
- Bayesian Inference
- Hypothesis Testing
- Distributions (Normal, Bernoulli, etc.)
- 📌 Example Project: Build a simple Naive Bayes classifier
Optimization Techniques
- Gradient Descent
- Convex Optimization
- Constraints & Lagrange Multipliers
- 📌 Example Project: Optimize a cost function with SciPy
🧩 Hands-On Projects
- Project 1: Linear Regression with Math
- Project 2: Neural Networks from Scratch
- Project 3: Math Behind Deep Learning
📚 Recommended Reading
For deeper insights into the mathematical foundations:
Let’s start coding! 🚀