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https://github.com/leehanchung/awesome-full-stack-machine-learning-courses

Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.
https://github.com/leehanchung/awesome-full-stack-machine-learning-courses

List: awesome-full-stack-machine-learning-courses

berkeley berkeley-ai berkeley-reinforcement-learning caltech columbia-university computer-science deep-learning deep-neural-networks edx-columbiax machine-learning reinforcement-learning stanford udemy

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Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.

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# Awesome Full Stack Machine Learning Engineering Courses

This is curated list of publicly accessible machine learning courses from top universities such as Berkeley, Harvard, Stanford, and MIT. It also includes machine learning project case studies from large and experienced companies. The list is broken down by topics and areas of specializations. Python is the preferred language of choice as it covers end-to-end machine learning engineering.



Special thanks to the schools to make their course videos and assignments publicly available.



## TL;DR

Bare minimum list of courses to go through for basic knowledge in machine learning engineering.



[MIT: The Missing Sememster of Your CS Education](https://missing.csail.mit.edu/)



[edX Harvard: CS50x: Introduction to Computer Science](https://www.edx.org/course/cs50s-introduction-to-computer-science)



[MIT 18.05: Introduction to Probability and Statistics](https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/)



[Columbia COMS W4995: Applied Machine Learning](https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/) [:tv:](https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM)



[MIT 18.06: Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)



[Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks](https://cs182sp21.github.io/): [:tv:](https://www.youtube.com/watch?v=rSY1pVGdZ4I&list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) [[Reference Solutions](https://github.com/leehanchung/cs182)]



[Berkeley: Full Stack Deep Learning](https://fullstackdeeplearning.com/)



## Computer Science



Foundational computer science, Python, and SQL skills for machine learning engineering.



### :books: Textbooks

[Grokking Algorithms](https://github.com/KevinOfNeu/ebooks/blob/master/Grokking%20Algorithms.pdf)



[Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)



[Python Design Patterns](https://github.com/faif/python-patterns)



[Python3 Patterns](https://python-3-patterns-idioms-test.readthedocs.io/en/latest/)



[Design Patterns: Elements of Reusable Object-Oriented Software 1st Edition](https://www.amazon.com/Design-Patterns-Elements-Reusable-Object-Oriented-dp-0201633612/dp/0201633612)



### :school: Courses

[MIT: The Missing Sememster of Your CS Education](https://missing.csail.mit.edu/) :star:



[edX MITX: Introduction to Computer Science and Programming Using Python](https://www.edx.org/course/6-00-1x-introduction-to-computer-science-and-programming-using-python-4) :star:



[edX Harvard: CS50x: Introduction to Computer Science](https://www.edx.org/course/cs50s-introduction-to-computer-science)



[SQL for Data Analysis](https://classroom.udacity.com/courses/ud198)



[PostgreSQL Exercises](https://pgexercises.com/)



[U Waterloo: CS794: Optimization for Data Science](https://cs.uwaterloo.ca/~y328yu/mycourses/794-2020/lecture.html)



[Berkeley CS 170: Efficient Algorithms and Intractable Problems](https://cs170.org/)



[Berkeley CS 294-165: Sketching Algorithms](https://www.sketchingbigdata.org/fall20/)



[MIT 6.824: Distributed Systems](http://nil.csail.mit.edu/6.824/2020/) [:tv:](https://www.youtube.com/watch?v=cQP8WApzIQQ&list=PLrw6a1wE39_tb2fErI4-WkMbsvGQk9_UB)



## Math and Statistics

Linear algebra and statistics



![math and machine learning](images/math_ml.jpg)



### :books: Textbooks



[NIST Engineering Statistics Handbook](https://www.itl.nist.gov/div898/handbook/)



#### :school: Courses

[MIT 18.05: Introduction to Probability and Statistics](https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/) :star:



[MIT 18.06: Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) :star:



[Stanford Stats216: Statiscal Learning](https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about) :star:



[CalTech: Learning From Data](https://work.caltech.edu/telecourse.html)



[A Students Guide to Bayesian Statistics](https://www.youtube.com/watch?v=P_og8H-VkIY&list=PLwJRxp3blEvZ8AKMXOy0fc0cqT61GsKCG)



[Introduction to Linear Algebra for Applied Machine Learning with Python](https://pabloinsente.github.io/intro-linear-algebra)



## Artificial Intelligence



Artificial Intelligence is the superset of Machine Learning. These courses provides a much higher level understanding of the field of AI, including searching, planning, logic, constrain optimization, and machine learning.



![artificial intelligence](images/artificial_intelligence.png)



#### :books: Textbooks



[Artificial Intelligence: A Modern Approach](https://www.amazon.com/Artificial-Intelligence-Modern-Approach-3rd/dp/0136042597)



#### :school: Courses



[Berkeley CS188: Artificial Intelligence](https://edge.edx.org/courses/course-v1:BerkeleyX+CS188+2018_SP/course/) :star:



[edX ColumbiaX: Artificial Intelligence](https://www.edx.org/course/artificial-intelligence-ai): [[Reference Solutions](https://github.com/leehanchung/CSMM-101x-AI)]



## Machine Learning



Machine learning.



![machine learning](images/machine_learning.png)



#### :books: Textbooks

[Mathematics for Machine Learning](https://mml-book.github.io/)



[Concise Machine Learning](https://people.eecs.berkeley.edu/~jrs/papers/machlearn.pdf)



[The Elements of Statistical Learning](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)



[Mining of Massive Datasets](http://www.mmds.org/)



[Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf): [[Codes](https://github.com/ctgk/PRML)]



[Cross-Industry Process for Data Mining methodology](ftp://public.dhe.ibm.com/software/analytics/spss/documentation/modeler/18.0/en/ModelerCRISPDM.pdf)



#### :school: Courses



[Columbia COMS W4995: Applied Machine Learning](https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/) [:tv:](https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM) :star:



[Stanford CS229: Machine Learning](https://see.stanford.edu/Course/CS229) [:tv:](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)



[Harvard CS 109A Data Science](https://harvard-iacs.github.io/2019-CS109A/pages/materials.html)



[edX ColumbiaX: Machine Learning](https://www.edx.org/course/machine-learning)



[Berkeley CS294: Fairness in Machine Learning](https://fairmlclass.github.io/)



[Google: Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course)



[Google: AI Education](https://ai.google/education/)



[Google: Applied Machine Learning Intensive](https://github.com/google/applied-machine-learning-intensive)



[Cornell Tech CS5785: Applied Machine Learning](https://cornelltech.github.io/cs5785-fall-2019/) [:tv:](https://www.youtube.com/playlist?list=PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83)



[Probabilistic Machine Learning (Summer 2020)](https://uni-tuebingen.de/de/180804) [:tv:](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd)



[AutoML - Automated Machine Learning](https://ki-campus.org/courses/automl-luh2021)\



[MIT: Data Centric AI](https://dcai.csail.mit.edu/)



## Machine Learning Engineering



These courses helps you bridge the gap from training machine learning models to deploy AI systems in the real world.



![production](images/production.jpg)



#### :books: Textbooks

[Machine Learning Engineering](http://www.mlebook.com/wiki/doku.php)



[Machine Learning System Design](https://huyenchip.com/machine-learning-systems-design/design-a-machine-learning-system.html)



[Microsoft Commercial Software Engineering ML Fundamentals](https://microsoft.github.io/code-with-engineering-playbook/ml-fundamentals/)



[Google Rules of ML](https://developers.google.com/machine-learning/guides/rules-of-ml)



[The Twelve Factors App](https://12factor.net/)



[Feature Engineering and Selection: A Practical Approach for Predictive Models](http://www.feat.engineering/a-simple-example.html)



[Continuous Delivery for Machine Learning](https://martinfowler.com/articles/cd4ml.html)



#### :school: Courses

[Berkeley: Full Stack Deep Learning](https://fullstackdeeplearning.com/) :star:



[Stanford: CS 329S: Machine Learning Systems Design](https://stanford-cs329s.github.io/syllabus.html) :star:



[CMU: Machine Learning in Production](https://ckaestne.github.io/seai/S2021/) [github](https://github.com/ckaestne/seai/)



[Andrew Ng: Bridging AI's Proof-of-Concept to Production Gap](https://www.youtube.com/watch?v=tsPuVAMaADY)



[Facebook Field Guide to Machine Learning](https://research.fb.com/blog/2018/05/the-facebook-field-guide-to-machine-learning-video-series/)



[Udemy: Deployment of Machine Learning Models](https://www.udemy.com/course/deployment-of-machine-learning-models) :star:



[Spark](https://classroom.udacity.com/courses/ud2002)



[Udemy: The Complete Hands On Course To Master Apache Airflow](https://www.udemy.com/course/the-complete-hands-on-course-to-master-apache-airflow)



## Deep Learning Overview



Basic overview for deep learning.



![deep learning](images/deep_learning.png)



#### :books: Textbooks

[Deep Learning](http://www.deeplearningbook.org/)



[Dive into Deep Learning](http://d2l.ai/index.html)



[The Matrix Calculus You Need For Deep Learning](https://arxiv.org/pdf/1802.01528.pdf)



#### :school: Courses

[Berkeley CS 182: Designing, Visualizing and Understanding Deep Neural Networks](https://cs182sp21.github.io/)



[Stanford CS 25: Transformers](https://web.stanford.edu/class/cs25/) [:tv:](https://www.youtube.com/watch?v=P127jhj-8-Y&list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM)



[Deeplearning.ai Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning): [[Reference Solutions](https://github.com/leehanchung/deeplearning.ai)] :star:



[NYU: Deep Learning](https://atcold.github.io/pytorch-Deep-Learning/)



## Specializations



### Recommendation Systems



Recommendation system is used when users do not know what they want and cannot use keywords to describe needs.



![youtube recommender](images/nobody-youtubes-recommendation-system.png)



#### :books: Textbooks

[Mining of Massive Datasets](http://www.mmds.org/)



[Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/)



[Dive into Deep Learning: Chapter 16 Recommender Systems](http://d2l.ai/chapter_recommender-systems/index.html)



#### :school: Courses



[Stanford CS246: Mining Massive Data Sets](http://web.stanford.edu/class/cs246/)



### Information Retrieval and Web Search



Search and Ranking is used when users have specific needs and can use keywords to describe their needs.



#### :books: Textbooks

[Introduction to Information Retrieval](https://nlp.stanford.edu/IR-book/)



#### :school: Courses



[Stanford CS224U: Natural Language Understanding - NLU and Information Retrieval](https://www.youtube.com/watch?v=Bn6RNrwwiI0&list=PLoROMvodv4rPt5D0zs3YhbWSZA8Q_DyiJ&index=38)



[TU Wein: Crash Course IR - Fundamentals](https://www.youtube.com/watch?v=6FNISntK6Sk&list=PLSg1mducmHTPZPDoal4m59pPxxsceXF-y)



[UIUC: Text Retrieval and Search Engines](https://www.youtube.com/playlist?list=PLLssT5z_DsK8Jk8mpFc_RPzn2obhotfDO)



[Stanford CS276: Information Retrieval and Web Search](http://web.stanford.edu/class/cs276/)



[University of Freiburg: Information Retrieval](https://ad-wiki.informatik.uni-freiburg.de/teaching/InformationRetrievalWS1718) [:tv:](https://www.youtube.com/watch?v=QjA7ujQsL0M&list=PLfgMNKpBVg4V8GtMB7eUrTyvITri8WF7i)



### Natural Language Processing



With languages models and sequential models, everyone can write like GPT-3.



![nlp](images/nlp.png)



#### :books: Textbook

[Deep Learning](http://www.deeplearningbook.org/)



[Introduction to Natural Language Processing](https://www.amazon.com/Introduction-Language-Processing-Adaptive-Computation/dp/0262042843)



[Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/)



#### :school: Courses

[Stanford CS224n: Natural Language Processing with Deep Learning](http://web.stanford.edu/class/cs224n/): [[Reference Solutions](https://github.com/leehanchung/cs224n)] :star:



[Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks](https://cs182sp21.github.io/): [:tv:](https://www.youtube.com/watch?v=rSY1pVGdZ4I&list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) [[Reference Solutions](https://github.com/leehanchung/cs182)]



[NYU: DS-GA 1011 Natural Language Processing with Representation Learnin](https://www.youtube.com/playlist?list=PLdH9u0f1XKW_s-c8EcgJpn_HJz5Jj1IRf)



[Deeplearning.ai Natural Language Processing Specialization](https://www.deeplearning.ai/natural-language-processing-specialization/) [[Reference Solutions](https://github.com/leehanchung/deeplearning.ai-nlp-specialization)]



### Vision



Neural nets cannot solve all vision problems, yet.



![computer vision](images/computer_vision.png)



#### :books: Textbooks

[Deep Learning](http://www.deeplearningbook.org/)



#### :school: Courses

[Stanford CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/): [[Assignment 2 Solution](https://github.com/leehanchung/cs182/tree/master/assignment1), [Assignment 3 Solution](https://github.com/leehanchung/cs182/tree/master/assignment2)] :star:



[Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks](https://cs182sp21.github.io/): [:tv:](https://www.youtube.com/watch?v=rSY1pVGdZ4I&list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A) [[Reference Solutions](https://github.com/leehanchung/cs182)]



### Unsupervised Learning and Generative Models



![gan](images/gan.png)



#### :school: Courses

[Stanford CS236: Deep Generative Models](https://deepgenerativemodels.github.io/)



[Berkeley CS294-158: Deep Unsupervised Learning](https://sites.google.com/view/berkeley-cs294-158-sp19/home)



### Foundation Models



![llm](images/FoO-U_gaYAAIlPZ.jpg)



[Stanford CS234: Large Language Models (Winter 2022)](https://stanford-cs324.github.io/winter2022/)



[Stanford CS234: Advances in Foundation Models (Winter 2023)](https://stanford-cs324.github.io/winter2023/)



### Reinforcement Learning



![rl](images/rl.jpg)



#### :books: Textbook



[Reinforcement Learning](http://www.incompleteideas.net/book/the-book.html)



[Deep Learning](http://www.deeplearningbook.org/)



#### :school: Courses

[Coursera: Reinforcement Learning Specialization](https://www.coursera.org/specializations/reinforcement-learning) <= Recommended by [Richard Sutton](https://www.reddit.com/r/MachineLearning/comments/h940xb/what_is_the_best_way_to_learn_about_reinforcement/), the author of the de facto textbook on RL. :star:



[Berkeley CS182: Designing, Visualizing, and Understanding Deep Neural Networks](https://cs182sp21.github.io/): [:tv:](https://www.youtube.com/watch?v=rSY1pVGdZ4I&list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A)  [[Reference Solutions](https://github.com/leehanchung/cs182)]



[Stanford CS234: Reinforcement Learning](https://web.stanford.edu/class/cs234/)



[Berkeley CS285: Deep Reinforcement Learning](http://rail.eecs.berkeley.edu/deeprlcourse/) :star:



[CS 330: Deep Multi-Task and Meta Learning](http://cs330.stanford.edu/): [Videos](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5)



[Berekley: Deep Reinforcement Learning Bootcamp](https://sites.google.com/view/deep-rl-bootcamp/lectures)



[OpenAI Spinning Up](https://spinningup.openai.com/en/latest/)



IDS at Stanford RL forum [Video 1](https://stanford.zoom.us/rec/share/3Xd-OxnFkFfXV3UBRGo68iScSbckWF-3OKuVQkEQc_igSL9JRyuwDvgXDArMHtFz.6s3GFT1XBvZf7eis?startTime=1610388191000) [Video 2](https://stanford.zoom.us/rec/share/8Ex0ug8ueM0G3DLAW4XLYTlhgV812fOkL5aUYjxes6JFysWglqa-FCNryj-GUn2a.21yA0Q1WPwhwZMgF?startTime=1610560965000) [Slides](https://drive.google.com/file/d/1KSFVptieJ-b115mLqAYfp2pVhJZ02qWh/view?usp=sharing)



### Robotics :robot:



Quaternions, quaternions everywhere. And gradients.



![robotics](images/robotics.png)



#### :school: Courses

[ColumbiaX: CSMM.103x Robotics](https://courses.edx.org/courses/course-v1:ColumbiaX+CSMM.103x+1T2020/)



[CS 287: Advanced Robotics](https://people.eecs.berkeley.edu/~pabbeel/cs287-fa19/)



# LICENSE

All books, blogs, and courses are owned by their respective authors.



You can use my compilation and my reference solutions under the open CC BY-SA 3.0 license and cite it as:

```

@misc{leehanchung,

  author = {Lee, Hanchung},

  title = {Full Stack Machine Learning Engineering Courses},

  year = {2020},

  howpublished = {Github Repo},

  url = {https://github.com/awesome-full-stack-machine-learning-courses}

}

```