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