# 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) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://irosyadi.gitbook.io/irosyadi/course/machine-learning-andrewng.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.