Machine learning online course from Andrew Ng.
- Lecture1 (week1): Introduction
- Lecture2 (week1): Linear Regression with One Variable
- Lecture3 (week1): Linear Algebra Review
- Lecture4 (week2): Linear Regression with Multiple Variables
- Lecture5 (week2): Octave/Matlab Tutorial
- Lecture6 (week3): Logistic Regression
- Lecture7 (week3): Regularization
- Lecture8 (week4): Neural Networks: Representation
- Lecture9 (week5): Neural Networks: Learning
- Lecture10 (week6): Advice for Applying Machine Learning
- Lecture11 (week6): Machine Learning System Design
- Lecture12 (week7): Support Vector Machines
- Lecture13 (week8): Unsupervised Learning
- Lecture14 (week8): Dimensionality Reduction
- Lecture15 (week9): Anomaly Detection
- Lecture16 (week9): Recommender Systems
- Lecture17 (week10): Large Scale Machine Learning
- Lecture18 (week11): Application Example: Photo OCR
- Programming Exercise 1: Linear Regression [python version]
- Programming Exercise 2: Logistic Regression [python version]
- Programming Exercise 3: Multi-class Classification and Neural Networks [python version]
- Programming Exercise 4: Neural Networks Learning [python version]
- Programming Exercise 5: Regularized Linear Regression and Bias v.s. Variance [python version]
- Programming Exercise 6: Support Vector Machines [python version]
- Programming Exercise 7: K-means Clustering and Principal Component Analysis [python version]
- Programming Exercise 8: Anomaly Detection and Recommender Systems [python version]