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

• Udemy Top Machine Learning Courses Online - Updated April 2021

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

Machine Learning Youtube

• StatQuest with Josh Stamer