# Machine Learning by Andrew Ng Resources ## Main Course * [Coursera : Machine Learning by Andrew Ng](https://www.coursera.org/learn/machine-learning/home/welcome) * [Youtube Playlists](https://www.youtube.com/watch?v=PPLop4L2eGk\&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN) * Video lectures Index * Programming Exercise Tutorials * Programming Exercise Test Cases * Useful Resources ## More Machine Learning Courses * [Udemy Top Machine Learning Courses Online - Updated April 2021](https://www.udemy.com/topic/machine-learning/) ## Suplementary Notes * [Holehouse Notes](https://www.holehouse.org/mlclass/) : review by holehouse * [Kaggle Notes](https://www.kaggle.com/getting-started/102365) * [Vkosuri Notes](https://vkosuri.github.io/CourseraMachineLearning/#programming-exercise-tutorials) : ppt, pdf, course, errata notes, [Github Repo](https://github.com/vkosuri/CourseraMachineLearning/tree/master/home) * [Danlu Zhang](https://danluzhang.com/2020/05/03/machine-learning-andrew-ng-notes/) : review by Danlu Zhang * [CSEAV](https://cseav.blogspot.com/) * [Stanford](https://github.com/mGalarnyk/datasciencecoursera/tree/master/Stanford_Machine_Learning) : quiz discussion ## Suplementary Codes * [Fengdu78](https://github.com/fengdu78/Coursera-ML-AndrewNg-Notes) : ppt, code in python (ipynb) * [dibgerge](https://github.com/dibgerge/ml-coursera-python-assignments) : assignment code in python (ipynb) * [Kaleko](https://github.com/kaleko/CourseraML) : assignment code in python (ipynb) * [nsoojin](https://github.com/nsoojin/coursera-ml-py) : code in python * [lucasshenv](https://github.com/lucasshenv/Coursera-ML-AndrewNg) : code in python (ipynb) using Tensorflow * [AvaisP](https://github.com/AvaisP/machine-learning-programming-assignments-coursera-andrew-ng) : assignment code in Octave * [Benlau93](https://github.com/Benlau93/Machine-Learning-by-Andrew-Ng-in-Python) : assignment code in Python * [worldveil](https://github.com/worldveil/coursera-ml): 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.](https://github.com/dibgerge/ml-coursera-python-assignments) ### Week 1: * Welcome - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-1/lectures/pdf/Lecture1.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-1/lectures/ppt/Lecture1.pptx) * Linear regression with one variable - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-1/lectures/pdf/Lecture2.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-1/lectures/ppt/Lecture2.pptx) * Linear Algebra review (Optional) - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-1/lectures/pdf/Lecture3.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-1/lectures/ppt/Lecture3.pptx) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-1/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-1/errata.pdf) * [Week 1 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-1-5-3-2020.pdf) * [01 and 02: Introduction, Regression Analysis and Gradient Descent by Holehouse](https://www.holehouse.org/mlclass/01_02_Introduction_regression_analysis_and_gr.html) * [03: Linear Algebra - review by Holehouse](https://www.holehouse.org/mlclass/03_Linear_algebra_review.html) * [adit.io: Linear Regression](https://adit.io/posts/2016-02-20-Linear-Regression-in-Pictures.html) ### Week 2: * Linear regression with multiple variables - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-2/lectures/pdf/Lecture4.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-2/lectures/ppt/Lecture4.pptx) * Octave tutorial [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-2/lectures/pdf/Lecture5.pdf) * Programming Exercise 1: Linear Regression - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-2/exercises/machine-learning-ex1/ex1.pdf) - [Problem](https://vkosuri.github.io/CourseraMachineLearning/home/week-2/exercises/machine-learning-ex1.zip) - [Solution](https://vkosuri.github.io/home/week-2/exercises/machine-learning-ex1/ex1/) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-2/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-2/errata.pdf) * [Program Exercise Notes](https://vkosuri.github.io/home/week-2/exercises/Programming%20Ex.1.pdf) * [Week 2 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-2-5-3-2020.pdf) * [04: Linear Regression with Multiple Variables by Holehouse](https://www.holehouse.org/mlclass/04_Linear_Regression_with_multiple_variables.html) * [05: Octave by Holehouse](https://www.holehouse.org/mlclass/05_Octave.html) ### Week 3: * Logistic regression - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/lectures/pdf/Lecture6.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/lectures/ppt/Lecture6.pptx) * Regularization - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/lectures/pdf/Lecture7.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/lectures/ppt/Lecture7.pptx) * Programming Exercise 2: Logistic Regression - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/exercises/machine-learning-ex2/ex2.pdf) - [Problem](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/exercises/machine-learning-ex2.zip) - [Solution](https://vkosuri.github.io/home/week-3/exercises/machine-learning-ex2/ex2) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-3/errata.pdf) * [Program Exercise Notes](https://vkosuri.github.io/home/week-3/exercises/Programming%20Ex.2.pdf) * [adit.io: Logistic Regression](https://adit.io/posts/2016-03-13-Logistic-Regression.html#non-linear-classification) * [Week 3 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-3-5-4-2020.pdf) * [06: Logistic Regression by Holehouse](https://www.holehouse.org/mlclass/06_Logistic_Regression.html) * [07: Regularization by Holehouse](https://www.holehouse.org/mlclass/07_Regularization.html) ### Week 4: * Neural Networks: Representation - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/lectures/pdf/Lecture8.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/lectures/ppt/Lecture8.pptx) * Programming Exercise 3: Multi-class Classification and Neural Networks - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/exercises/machine-learning-ex3/ex3.pdf) - [Problem](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/exercises/machine-learning-ex3.zip) - [Solution](https://vkosuri.github.io/home/week-4/exercises/machine-learning-ex3/ex3) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-4/errata.pdf) * [Program Exercise Notes](https://vkosuri.github.io/home/week-4/exercises/Programming%20Ex.3.pdf) * [Week 4 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-4-5-5-2020.pdf) * [08: Neural Networks - Representation by Holehouse](https://www.holehouse.org/mlclass/08_Neural_Networks_Representation.html) ### Week 5: * Neural Networks: Learning - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/lectures/pdf/Lecture9.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/lectures/ppt/Lecture9.pptx) * Programming Exercise 4: Neural Networks Learning - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/exercises/machine-learning-ex4/ex4.pdf) - [Problem](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/exercises/machine-learning-ex4.zip) - [Solution](https://vkosuri.github.io/home/week-5/exercises/machine-learning-ex4/ex4) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-5/errata.pdf) * [Program Exercise Notes](https://vkosuri.github.io/home/week-4/exercises/Programming%20Ex.4.pdf) * [Week 5 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-5-5-6-2020.pdf) * [09: Neural Networks - Learning by Holehouse](https://www.holehouse.org/mlclass/09_Neural_Networks_Learning.html) ### Week 6: * Advice for applying machine learning - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-6/lectures/pdf/Lecture10.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-6/lectures/ppt/Lecture10.pptx) * Machine learning system design - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-6/lectures/pdf/Lecture11.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-6/lectures/ppt/Lecture11.pptx) * Programming Exercise 5: Regularized Linear Regression and Bias v.s. Variance - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-6/exercises/machine-learning-ex5/ex5.pdf) - [Problem](https://vkosuri.github.io/CourseraMachineLearning/home/week-6/exercises/machine-learning-ex5.zip) - [Solution](https://vkosuri.github.io/home/week-6/exercises/machine-learning-ex5/ex5) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-6/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-6/errata.pdf) * [Program Exercise Notes](https://vkosuri.github.io/home/week-6/exercises/Programming%20Ex.5.pdf) * [Week 6 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-6-5-6-2020.pdf) * [10: Advice for applying machine learning techniques by Holehouse](https://www.holehouse.org/mlclass/10_Advice_for_applying_machine_learning.html) * [11: Machine Learning System Design by Holehouse](https://www.holehouse.org/mlclass/11_Machine_Learning_System_Design.html) ### Week 7: * Support vector machines - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-7/lectures/pdf/Lecture12.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-7/lectures/ppt/Lecture12.pptx) * Programming Exercise 6: Support Vector Machines - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-7/exercises/machine-learning-ex6/ex6.pdf) - [Problem](https://vkosuri.github.io/CourseraMachineLearning/home/week-7/exercises/machine-learning-ex6.zip) - [Solution](https://vkosuri.github.io/home/week-7/exercises/machine-learning-ex6/ex6) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-7/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-7/errata.pdf) * [Program Exercise Notes](https://vkosuri.github.io/home/week-7/exercises/Programming%20Ex.6.pdf) * [Week 7 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-7-5-7-2020.pdf) * [12: Support Vector Machines by Holehouse](https://www.holehouse.org/mlclass/12_Support_Vector_Machines.html) ### Week 8: * Clustering - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-8/lectures/pdf/Lecture13.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-8/lectures/ppt/Lecture13.ppt) * Dimensionality reduction - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-8/lectures/pdf/Lecture14.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-8/lectures/ppt/Lecture14.ppt) * Programming Exercise 7: K-means Clustering and Principal Component Analysis - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-8/exercises/machine-learning-ex7/ex7.pdf) - [Problems](https://vkosuri.github.io/CourseraMachineLearning/home/week-8/exercises/machine-learning-ex7.zip) - [Solution](https://vkosuri.github.io/home/week-8/exercises/machine-learning-ex7/ex7) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-8/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-8/errata.pdf) * [Program Exercise Notes](https://vkosuri.github.io/home/week-8/exercises/Programming%20Ex.7.pdf) * [Week 8 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-8-5-7-2020.pdf) * [13: Clustering by Holehouse](https://www.holehouse.org/mlclass/13_Clustering.html) * [14: Dimensionality Reduction by Holehouse](https://www.holehouse.org/mlclass/14_Dimensionality_Reduction.html) ### Week 9: * Anomaly Detection - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-9/lectures/pdf/Lecture15.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-9/lectures/ppt/Lecture15.ppt) * Recommender Systems - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-9/lectures/pdf/Lecture16.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-9/lectures/ppt/Lecture16.ppt) * Programming Exercise 8: Anomaly Detection and Recommender Systems - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-9/exercises/machine-learning-ex8/ex8.pdf) - [Problems](https://vkosuri.github.io/CourseraMachineLearning/home/week-9/exercises/machine-learning-ex8.zip) - [Solution](https://vkosuri.github.io/home/week-9/exercises/machine-learning-ex8/ex8) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-9/lectures/notes.pdf) * [Errata](https://vkosuri.github.io/CourseraMachineLearning/home/week-9/errata.pdf) * [Program Exercise Notes](https://vkosuri.github.io/home/week-9/exercises/Programming%20Ex.8.pdf) * [Week 9 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-9-5-7-2020-1.pdf) * [15: Anomaly Detection by Holehouse](https://www.holehouse.org/mlclass/15_Anomaly_Detection.html) * [16: Recommender Systems by Holehouse](https://www.holehouse.org/mlclass/16_Recommender_Systems.html) ### Week 10: * Large scale machine learning - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-10/lectures/pdf/Lecture17.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-10/lectures/ppt/Lecture17.ppt) * [Lecture Notes](https://vkosuri.github.io/CourseraMachineLearning/home/week-10/lectures/notes.pdf) * [Week 10 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-10-5-9-2020.pdf) * [17: Large Scale Machine Learning by Holehouse](https://www.holehouse.org/mlclass/17_Large_Scale_Machine_Learning.html) ### Week 11: * Application example: Photo OCR - [pdf](https://vkosuri.github.io/CourseraMachineLearning/home/week-11/lectures/pdf/Lecture18.pdf) - [ppt](https://vkosuri.github.io/CourseraMachineLearning/home/week-11/lectures/ppt/Lecture18.ppt) * [Week 11 by danluzhang](https://danluzhang.com/wp-content/uploads/2020/05/Notes-on-Machine-Learning_Andrew-Ng_Week-11-5-9-2020.pdf) * [18: Application Example - Photo OCR by Holehouse](https://www.holehouse.org/mlclass/18_Application_Example_OCR.html) * [19: Course Summary by Holehouse](https://www.holehouse.org/mlclass/19_Course_Summary.html) ## Extra Information * [Linear Algebra Review and Reference Zico Kolter](https://vkosuri.github.io/CourseraMachineLearning/extra/cs229-linalg.pdf) * [CS229 Lecture notes](https://vkosuri.github.io/CourseraMachineLearning/extra/cs229-notes1.pdf) * [CS229 Problems](https://vkosuri.github.io/CourseraMachineLearning/extra/cs229-prob.pdf) * [Financial time series forecasting with machine learning techniques](https://vkosuri.github.io/extra/machine%20learning%20stocks.pdf) * [Octave Examples](https://vkosuri.github.io/CourseraMachineLearning/extra/octave_session.m) ## Machine Learning Online E Books * [Introduction to Machine Learning by Nils J. Nilsson](https://ai.stanford.edu/~nilsson/mlbook.html) free * [Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan](https://alex.smola.org/drafts/thebook.pdf) free * [Introduction to Data Science by Jeffrey Stanton](https://surface.syr.edu/cgi/viewcontent.cgi?article=1165\&context=istpub) free * [Bayesian Reasoning and Machine Learning by David Barber](https://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online) free * [Understanding Machine Learning, © 2014 by Shai Shalev-Shwartz and Shai Ben-David](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/copy.html) free * [Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman](https://statweb.stanford.edu/~tibs/ElemStatLearn/) free * [Pattern Recognition and Machine Learning, by Christopher M. Bishop](https://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf) free, used * Master Machine Learning Algorithms: Discover How They Work and Implement Them From Scratch\ Jason Brownlee, proprietary, used * [Course in Machine Learning](https://ciml.info/) free, used ## Machine Learning Tutorial * [Trekhleb](https://github.com/trekhleb/machine-learning-octave) Machine Learning with Octave, free, used * [Trekhleb](https://github.com/trekhleb/homemade-machine-learning) Machine Learning with Python, free, used * [Trekhleb](https://github.com/trekhleb/machine-learning-experiments) Deep Learning with Python, free, used * [Tutorials Point: Machine Learning with Python](https://www.tutorialspoint.com/machine_learning_with_python/), used * [ML Cheatsheet](https://ml-cheatsheet.readthedocs.io/en/latest/index.html) free, used ## Machine Learning Youtube * [StatQuest with Josh Stamer](https://www.youtube.com/c/joshstarmer/playlists)