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

3Blue1Brown: Essence of Calculus

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

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/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

New to Deep Reinforcement Learning


Relevant Papers

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