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).
If you are completely new to everything, then you will need to start with some math and programming basics.
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
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/
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
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/
https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about
https://www.probabilitycourse.com
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
https://www.lynda.com/Python-tutorials/Python-Data-Science-Essential-Training/520233-2.html
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
https://github.com/jakevdp/sklearn_tutorial
https://developers.google.com/machine-learning/crash-course/
https://machinelearningmastery.com
http://www.3blue1brown.com/videos/2017/10/9/neural-network
https://blog.paperspace.com/a-practical-guide-to-deep-learning-in-6-months/
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
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/
https://www.youtube.com/watch?v=BR9h47Jtqyw
https://spinningup.openai.com/en/latest/spinningup/keypapers.html