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:
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:
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:
New to Deep Learning
Approach to Grokking Deep Learning
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:
