Free books, courses, and tools for data science, machine learning, and artificial intelligence. From beginner-friendly introductions to advanced deep learning — all openly accessible.
The authoritative textbook on deep learning by three pioneers in the field. Covers linear algebra, probability, neural networks, CNNs, RNNs, and modern techniques. Free to read online from MIT Press.
Essential tools for working with data in Python by Jake VanderPlas. Covers IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn with practical examples. Free to read online.
A widely-used textbook by James, Witten, Hastie, and Tibshirani. Covers regression, classification, resampling, tree-based methods, SVMs, and unsupervised learning. Free PDF from the authors.
A top-down approach to deep learning by Jeremy Howard and Sylvain Gugger. Build real models from day one using PyTorch and fastai. Full course + free Jupyter notebooks.
The world's largest data science community. Over 300,000 free datasets, notebooks, and competitions. Download CSVs or run analysis in browser. Export results as PDF reports.
Beautiful visual explanations of neural networks, backpropagation, and gradient descent by Grant Sanderson. Companion to the famous YouTube series. Supplementary notes available as PDF.
Learn R and the tidyverse for data analysis by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. Covers data import, tidy, transform, visualize, and model. Free to read online.
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