The handouts and exercises of Andrew Ng's Machine Learning course
Lecture1 Introduction
Lecture2 Linear regression with one variable
Lecture3 Linear Algebra review(optional)
Lecture4 Linear Regression with Multiple Variables
Lecture5 Octave/Matlab Tutorial
machine-learning-ex1 Linear Regression
Lecture6 Logistic Regression
Lecture7 Regularization
machine-learning-ex2 Logistic Regression
Lecture8 Neural Networks: Representation
machine-learning-ex3 Multi-class Classification and Neural Networks
Lecture9 Neural Networks: Learning
machine-learning-ex4 Neural Networks Learning
Lecture10 Advice for Applying Machine Learning
Lecture11 Machine learning system design
machine-learning-ex5 Regularized Linear Regression and Bias/Variance
Lecture12 Support Vector Machines
machine-learning-ex6 Support Vector Machines
Lecture13 Unsupervised Learning
Lecture14 Dimensionality Reduction
machine-learning-ex7 K-Means Clustering and PCA
Lecture15 Anomaly detection
Lecture16 Recommender Systems
machine-learning-ex8 Anomaly Detection and Recommender Systems
Lecture17 Large scale machine learning
Lecture18 Application Example: Photo OCR