### TOC

# Machine Learning by Andrew Ng Resources

## Main Course

- Coursera : Machine Learning by Andrew Ng
- Youtube Playlists
- Video lectures Index https://class.coursera.org/ml/lecture/preview
- Programming Exercise Tutorials https://www.coursera.org/learn/machine-learning/discussions/all/threads/m0ZdvjSrEeWddiIAC9pDDA
- Programming Exercise Test Cases https://www.coursera.org/learn/machine-learning/discussions/all/threads/0SxufTSrEeWPACIACw4G5w
- Useful Resources https://www.coursera.org/learn/machine-learning/resources/NrY2G

## More Machine Learning Courses

## Suplementary Notes

- Holehouse Notes : review by holehouse
- Kaggle Notes
- Vkosuri Notes : ppt, pdf, course, errata notes, Github Repo
- Danlu Zhang : review by Danlu Zhang
- CSEAV
- Stanford : quiz discussion

## Suplementary Codes

- Fengdu78 : ppt, code in python (ipynb)
- dibgerge : assignment code in python (ipynb)
- Kaleko : assignment code in python (ipynb)
- nsoojin : code in python
- lucasshenv : code in python (ipynb) using Tensorflow
- AvaisP : assignment code in Octave
- Benlau93 : assignment code in Python
- worldveil: code, pdf
- dibgerge/ml-coursera-python-assignments: Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.

### Week 1:

- Welcome - pdf - ppt
- Linear regression with one variable - pdf - ppt
- Linear Algebra review (Optional) - pdf - ppt
- Lecture Notes
- Errata
- Week 1 by danluzhang
- 01 and 02: Introduction, Regression Analysis and Gradient Descent by Holehouse
- 03: Linear Algebra - review by Holehouse
- adit.io: Linear Regression

### Week 2:

- Linear regression with multiple variables - pdf - ppt
- Octave tutorial pdf
- Programming Exercise 1: Linear Regression - pdf - Problem - Solution
- Lecture Notes
- Errata
- Program Exercise Notes
- Week 2 by danluzhang
- 04: Linear Regression with Multiple Variables by Holehouse
- 05: Octave by Holehouse

### Week 3:

- Logistic regression - pdf - ppt
- Regularization - pdf - ppt
- Programming Exercise 2: Logistic Regression - pdf - Problem - Solution
- Lecture Notes
- Errata
- Program Exercise Notes
- adit.io: Logistic Regression
- Week 3 by danluzhang
- 06: Logistic Regression by Holehouse
- 07: Regularization by Holehouse

### Week 4:

- Neural Networks: Representation - pdf - ppt
- Programming Exercise 3: Multi-class Classification and Neural Networks - pdf - Problem - Solution
- Lecture Notes
- Errata
- Program Exercise Notes
- Week 4 by danluzhang
- 08: Neural Networks - Representation by Holehouse

### Week 5:

- Neural Networks: Learning - pdf - ppt
- Programming Exercise 4: Neural Networks Learning - pdf - Problem - Solution
- Lecture Notes
- Errata
- Program Exercise Notes
- Week 5 by danluzhang
- 09: Neural Networks - Learning by Holehouse

### Week 6:

- Advice for applying machine learning - pdf - ppt
- Machine learning system design - pdf - ppt
- Programming Exercise 5: Regularized Linear Regression and Bias v.s. Variance - pdf - Problem - Solution
- Lecture Notes
- Errata
- Program Exercise Notes
- Week 6 by danluzhang
- 10: Advice for applying machine learning techniques by Holehouse
- 11: Machine Learning System Design by Holehouse

### Week 7:

- Support vector machines - pdf - ppt
- Programming Exercise 6: Support Vector Machines - pdf - Problem - Solution
- Lecture Notes
- Errata
- Program Exercise Notes
- Week 7 by danluzhang
- 12: Support Vector Machines by Holehouse

### Week 8:

- Clustering - pdf - ppt
- Dimensionality reduction - pdf - ppt
- Programming Exercise 7: K-means Clustering and Principal Component Analysis - pdf - Problems - Solution
- Lecture Notes
- Errata
- Program Exercise Notes
- Week 8 by danluzhang
- 13: Clustering by Holehouse
- 14: Dimensionality Reduction by Holehouse

### Week 9:

- Anomaly Detection - pdf - ppt
- Recommender Systems - pdf - ppt
- Programming Exercise 8: Anomaly Detection and Recommender Systems - pdf - Problems - Solution
- Lecture Notes
- Errata
- Program Exercise Notes
- Week 9 by danluzhang
- 15: Anomaly Detection by Holehouse
- 16: Recommender Systems by Holehouse

### Week 10:

- Large scale machine learning - pdf - ppt
- Lecture Notes
- Week 10 by danluzhang
- 17: Large Scale Machine Learning by Holehouse

### Week 11:

- Application example: Photo OCR - pdf - ppt
- Week 11 by danluzhang
- 18: Application Example - Photo OCR by Holehouse
- 19: Course Summary by Holehouse

## Extra Information

- Linear Algebra Review and Reference Zico Kolter
- CS229 Lecture notes
- CS229 Problems
- Financial time series forecasting with machine learning techniques
- Octave Examples

## Machine Learning Online E Books

- Introduction to Machine Learning by Nils J. Nilsson free
- Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan free
- Introduction to Data Science by Jeffrey Stanton free
- Bayesian Reasoning and Machine Learning by David Barber free
- Understanding Machine Learning, © 2014 by Shai Shalev-Shwartz and Shai Ben-David free
- Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman free
- Pattern Recognition and Machine Learning, by Christopher M. Bishop free, used
- Master Machine Learning Algorithms: Discover How They Work and Implement Them From Scratch Jason Brownlee, proprietary, used
- Course in Machine Learning free, used

## Machine Learning Tutorial

- Trekhleb Machine Learning with Octave, free, used
- Trekhleb Machine Learning with Python, free, used
- Trekhleb Deep Learning with Python, free, used
- Tutorials Point: Machine Learning with Python, used
- ML Cheatsheet free, used