AI
Machine Learning
5 resources
Foundations of Data Science
Avrim Blum, John Hopcroft, Ravindran Kannan
The mathematical and algorithmic underpinnings of data science: high-dimensional geometry, singular value decomposition, random graphs, clustering, and machine-learning theory, aimed at readers who want to understand why the methods work.
Machine LearningAdvancedOfficial Free DistributionPDF · 3.5 MBMachine Learning Crash Course
Google
Google's practical, example-driven introduction to machine learning: loss and gradient descent, classification, neural networks, embeddings, fairness, and production ML systems, with interactive exercises using TensorFlow.
Machine LearningBeginnerAPACHE 2.0HTML
An Introduction to Statistical Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
A broad, applied introduction to statistical learning: linear and logistic regression, resampling, regularization, trees and ensembles, support vector machines, and unsupervised learning, with lab exercises in R and Python.
Machine LearningIntermediateOfficial Free DistributionPDF
Mathematics for Machine Learning
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
The mathematical foundations behind machine learning, taught for ML rather than in the abstract: linear algebra, analytic geometry, matrix decompositions, vector calculus, probability, and optimization, connected to real ML models.
Machine LearningAdvancedOfficial Free DistributionPDF · 17 MB
Python Data Science Handbook
Jake VanderPlas
A practical guide to the core Python data-science stack: IPython/Jupyter, NumPy, Pandas, Matplotlib, and Scikit-Learn, with real workflows for cleaning, transforming, visualizing, and modeling data.
Machine LearningIntermediateMITHTML