Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
Machine Learning Path Recommendations

This is an incomplete, ever-changing curated list of content to assist people into the worlds of Data Science and Machine Learning. If you have a recommendation for something to add, please let me know. If something isn't here, it doesn't mean I don't recommend it, I just may not have had a chance to review it yet or not.

I will generally list things in order of easier to more formal/challenging content.

It may feel like there is an overwhelming amount of stuff for you to learn (because there is). But, there is a guided path that will get you there in time. You need to focus on Linear Algebra, Calculus, Statistics and probably Python (or R). Your best bet is to get a Safari Books Online account (https://www.safaribooksonline.com) which you may already have access to through school or work. If not, it is a reasonable way to get access to a tremendous number of books and videos.

I'm not saying you will get what you need out of everything here, but I have read/watched at least some of all of the following and have found them useful. Use your brain, the more expensive books are going to be more formal/academic. The O'Reilly books will be more developer friendly. Some of the self-published Kindle books are of varying quality but may still have some interesting examples (and are usually very cheap or free through Kindle Unlimited).

New to Everything

If you are completely new to everything, then you will need to start with some math and programming basics.

Books:

Review your Algebra and Trigonometry: https://www.amazon.com/Algebra-Trigonometry-Prepare-Calculus-College/dp/1523959614

Calculus: https://www.amazon.com/Calculus-Intuitive-Physical-Approach-Mathematics-ebook/dp/B00CB2MK6C

Linear Algebra: https://www.amazon.com/Linear-Algebra-Step-Kuldeep-Singh/dp/0199654441

Courses:

Videos:

3Blue1Brown: Essence of Linear Algebra https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

3Blue1Brown: Essence of Calculus https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr

https://ocw.mit.edu/resources/res-18-006-calculus-revisited-single-variable-calculus-fall-2010/

https://ocw.mit.edu/resources/res-18-007-calculus-revisited-multivariable-calculus-fall-2011/

https://ocw.mit.edu/resources/res-18-008-calculus-revisited-complex-variables-differential-equations-and-linear-algebra-fall-2011/

https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/video-lectures/

Sites:

Learn Python: http://www.learnpython.org

David Beazley's Excellent Python Tutorials: https://dabeaz-course.github.io/practical-python/

Google's Python class: https://developers.google.com/edu/python/

Learn R: http://tryr.codeschool.com

Self-directed R tutorial: https://cran.r-project.org/doc/manuals/r-release/R-intro.html

https://openstax.org/subjects/math

New to Statistics

Books:

https://www.amazon.com/Practical-Statistics-Data-Scientists-Essential/dp/1491952962

http://www.greenteapress.com/thinkstats/

https://www.amazon.com/Seven-Pillars-Statistical-Wisdom/dp/0674088913

https://www.amazon.com/Hypothesis-Testing-Introduction-Statistical-Significance-ebook/dp/B019N212NE

https://www.amazon.com/Introductory-Statistics-R-Computing/dp/0387790535

http://www-bcf.usc.edu/~gareth/ISL/

https://www.amazon.com/Computer-Age-Statistical-Inference-Mathematical/dp/1107149894

https://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576

http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf

https://web.stanford.edu/~hastie/ElemStatLearn/

Sites:

https://www.openintro.org

Courses:

https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about

https://www.probabilitycourse.com

New to Data Science

Books:

https://www.amazon.com/Bad-Data-Handbook-Cleaning-Back/dp/1449321887

https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662

https://www.amazon.com/Doing-Data-Science-Straight-Frontline/dp/1449358659

https://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X

https://jakevdp.github.io/PythonDataScienceHandbook/

https://www.amazon.com/Data-Science-Mindset-Methodologies-Misconceptions-ebook/dp/B074R7HL2W

Courses:

https://www.lynda.com/Python-tutorials/Python-Data-Science-Essential-Training/520233-2.html

New to Machine Learning

Books:

https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists/dp/1449369413

https://www.amazon.com/Machine-Learning-Hackers-Studies-Algorithms/dp/1449303714

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

https://www.amazon.com/Bayesian-Methods-Hackers-Probabilistic-Addison-Wesley/dp/0133902838

https://www.amazon.com/Think-Bayes-Bayesian-Statistics-Python/dp/1449370780

https://www.amazon.com/Understanding-Machine-Learning-Theory-Algorithms/dp/1107057132

Courses:

https://github.com/dair-ai/ML-YouTube-Courses

https://github.com/jakevdp/sklearn_tutorial

https://developers.google.com/machine-learning/crash-course/

https://machinelearningmastery.com

Videos:

http://www.3blue1brown.com/videos/2017/10/9/neural-network

New to Deep Learning

Approach to Grokking Deep Learning

https://blog.paperspace.com/a-practical-guide-to-deep-learning-in-6-months/

Books:

https://www.amazon.com/Deep-Learning-Illustrated-Intelligence-Addison-Wesley/dp/0135116694/

https://www.manning.com/books/deep-learning-with-python

https://www.manning.com/books/deep-learning-with-javascript

https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine-ebook/dp/B01MRVFGX4

Sites:

http://neuralnetworksanddeeplearning.com

http://www.deeplearningpatterns.com/doku.php?id=overview

http://jalammar.github.io/visual-interactive-guide-basics-neural-networks/

https://jalammar.github.io/feedforward-neural-networks-visual-interactive/

Videos:

https://sebastianraschka.com/blog/2021/dl-course.html

https://www.youtube.com/watch?v=BR9h47Jtqyw

New to Deep Reinforcement Learning

Relevant Papers

https://spinningup.openai.com/en/latest/spinningup/keypapers.html

@iswarup

This comment has been minimized.

Copy link

@iswarup iswarup commented Jan 6, 2018

Fast.ai and deeplearning.ai

@matthewmccullough

This comment has been minimized.

Copy link

@matthewmccullough matthewmccullough commented Apr 25, 2018

While admittedly near-adjacent, https://aischool.microsoft.com/ has served some folks well from a few field reports.

@micmmakarov

This comment has been minimized.

Copy link

@micmmakarov micmmakarov commented Oct 13, 2018

Would you be able to recommend any podcasts that you like? Thanks :)

@OddExtension5

This comment has been minimized.

Copy link

@OddExtension5 OddExtension5 commented Sep 23, 2019

Would you be able to recommend any podcasts that you like? Thanks :)

According to me the best podcasts for Machine Learning, Data Science and Statistics are:

  • Data Skeptic
  • Stats + Stories
  • Machine Learning - Software Engineering Daily

You can find all these podcasts on Player FM app in play store.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment