Machine Learning Specialization — Study Notes & Labs

Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng.

---

📂 Repository Structure

Machine-Learning/
├── Supervised Machine Learning - Regression and Classification/
│   └── (lab notebooks — Week 1–3)
├── Advanced Learning Algorithms/
│   └── (lab notebooks — Week 4–6)
├── Unsupervised Learning, Recommenders, Reinforcement Learning/
│   └── (lab notebooks — Week 7–9)
├── notes/
│   ├── main.tex                  ← LaTeX root file
│   ├── main.pdf                  ← compiled PDF (auto-updated by CI)
│   └── chapters/
│       ├── ch01.tex
│       ├── ch02.tex
│       ├── ch03.tex
│       ├── ch04.tex
│       ├── ch05.tex
│       ├── ch06.tex
│       ├── ch07.tex
│       ├── ch08.tex
│       ├── ch09.tex
│       └── ch10.tex
├── .github/
│   └── workflows/
│       └── compile-latex.yml     ← auto-compile PDF on push
├── .gitignore
└── README.md

---

📖 Lecture Notes

A self-compiled LaTeX textbook covering all 10 chapters of the specialization — from linear regression to reinforcement learning.

📄 Read the full PDF: notes/main.pdf

Table of Contents

Chapter	Title	Topics
1	Introduction to Machine Learning	Overview of ML · Supervised vs. Unsupervised Learning · Linear Regression · Gradient Descent
2	Linear Regression with Multiple Variables	Multiple Features · Vectorisation · Feature Engineering · Feature Scaling · Convergence
3	Classification	Logistic Regression · Sigmoid Function · Decision Boundaries · Cross-Entropy Loss · Overfitting & Regularisation
4	Neural Networks	Biological Intuition · Layered Architecture · TensorFlow Implementation · Forward Propagation in NumPy
5	Neural Network Training	Training Framework · Activation Functions · Multiclass & Multi-label Classification · Adam Optimiser · Backpropagation
6	Advice for Applying Machine Learning	Bias & Variance · Train/CV/Test Split · Debugging Strategies · Data Augmentation · Transfer Learning · Skewed Datasets
7	Decision Trees	Entropy & Information Gain · Recursive Splitting · Tree Ensembles · Random Forests · XGBoost
8	Unsupervised Learning	K-Means Clustering · Anomaly Detection · Gaussian Density Model
9	Recommender Systems	Collaborative Filtering · Matrix Factorisation · Content-Based Filtering · Principal Component Analysis
10	Reinforcement Learning	MDPs · Q-Function · Bellman Equation · Deep Q-Networks · ε-Greedy Policy

---

🧪 Lab Notebooks

Hands-on programming assignments organised by course:

Course	Folder
Supervised Machine Learning: Regression and Classification	Supervised Machine Learning - R.../
Advanced Learning Algorithms	Advanced Learning Algorithms/
Unsupervised Learning, Recommenders, Reinforcement Learning	Unsupervised Learning/

---

⚙️ CI/CD — Auto-compile LaTeX

Every push to notes/ automatically triggers a GitHub Actions workflow that:

1. Installs TeX Live on an Ubuntu runner
2. Compiles main.tex with pdflatex
3. Commits the updated main.pdf back to the repository

No local LaTeX installation required.

---

🙏 Acknowledgements

This set of notes represents my personal journey through the Machine Learning Specialization — 10 weeks of learning, countless hours of problem sets, and a deep appreciation for how elegant mathematics can be when applied to real-world problems.

I would like to express my sincere gratitude to Professor Andrew Ng and the DeepLearning.AI team for designing such a thoughtful and accessible curriculum. The way complex concepts are broken down — from gradient descent to reinforcement learning — made this an incredibly rewarding experience.

To anyone reading these notes: I hope they serve as a useful companion, whether you are working through the specialization yourself, revisiting core concepts, or simply exploring the field of machine learning. These notes are not a substitute for the course itself — I strongly encourage you to take it firsthand.

If you find any errors or have suggestions for improvement, feel free to open an issue or pull request. Feedback is always welcome.

Happy learning. — Truong Dat, 2026

---

📚 Reference

Ng, A., Shyu, E., Bagul, A., & Ladwig, G. (2022). Machine Learning Specialization [Online course]. DeepLearning.AI & Stanford Online, Coursera. https://www.coursera.org/specializations/machine-learning-introduction

---

🏆 Certification

---