Online Courses
Machine Learning Course by Andrew Ng, Coursera (https://www.coursera.org/learn/machine-learning)
Coursera Data Science Offerings (https://www.coursera.org/browse/data-science)
Harvardx (https://www.edx.org/professional-certificate/harvardx-data-science)
MITx (https://www.edx.org/micromasters/mitx-statistics-and-data-science)
edX: Getting Started with Python (https://www.edx.org/course/programming-for-everybody-getting-started-with-pyt)
Harvard Statistics 110: Probability (https://projects.iq.harvard.edu/stat110/youtube)
https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo (another set of videos)
Books
Build a career in data science by Emily Robinson and Jacqueline Nolis (https://www.amazon.in/Build-Career-Science-Emily-Robinson/dp/1617296244/ref=sr_1_1?crid=1CXN8JQTX61Y5&dchild=1&keywords=build+a+career+in+data+science&qid=1600802928&sprefix=Build+a+career%2Caps%2C193&sr=8-1)
Free Books Available from the Authors Pages
Machine Learning Books
Introduction to Probability, Statistics and Random Processes, by Hossein Pishro-Nik (https://www.probabilitycourse.com/)
An Introduction to Statistical Learning: with Applications in R by James, Witten and Hastie (https://statlearning.com/)
Probabilistic Machine Learning: An Introduction, by Kevin Patrick Murphy https://probml.github.io/pml-book/book1.html
Pattern Recognition and Machine Learning, by Christopher M. Bishop (https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)
Bayesian Reasoning and Machine Learning by David Barber (http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online)
D.P. Kroese, Z.I. Botev, T. Taimre, R. Vaisman. Data Science and Machine Learning: Mathematical and Statistical Methods, Chapman and Hall/CRC, Boca Raton, 2019. https://people.smp.uq.edu.au/DirkKroese/DSML/
Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto (http://incompleteideas.net/book/the-book.html)
Deep Learning Books
Dive into Deep Learning (https://d2l.ai/)
Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville (https://www.deeplearningbook.org/)
Graph Representational Learning (https://www.cs.mcgill.ca/~wlh/grl_book/)
Python & R Books
Python for Everybody by Charles Severance (https://www.py4e.com/book) (Free book, with associated courses and videos)
Python Data Science Handbook (https://jakevdp.github.io/PythonDataScienceHandbook/)
R for Data Science by Garrett Grolemund and Hadley Wickham https://r4ds.had.co.nz/
Get Started with Julia (https://julialang.org/learning/)
Causal Inference Books
Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. (https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)
Other Books
Convex Optimization by Boyd and Vandenberghe (https://web.stanford.edu/~boyd/cvxbook/)
Algorithms for Optimization, by Mykel J. Kochenderfer and Tim A. Wheeler
Statistical Rethinking, by Richard McElreath (https://xcelab.net/rm/statistical-rethinking/)
Free Computing Resources
Google Colab (https://colab.research.google.com/notebooks/intro.ipynb#recent=true)
Microsoft Azure (https://azure.microsoft.com/en-us/free/students/)
Blogs
https://www.tableau.com/learn/articles/blogs-about-machine-learning-artificial-intelligence
Datasets & Projects
Kaggle Data (https://www.kaggle.com/datasets)
Deep Learning Frameworks
Pytorch (https://pytorch.org/)
TensorFlow (https://www.tensorflow.org/)
JAX (https://jax.readthedocs.io/en/latest/notebooks/quickstart.html)