machine learning Andrew Ng

Machine learning online course from Andrew Ng.

slides

• 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

homework

• 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]

videos & PPT

https://pan.baidu.com/s/1n2zg0tW2RA_4HGIgZsuk3w 00yr

additional materials (in Chinese)

https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes