(Key: π pdf file π html book)
- π "Python for Data Analysis" by Wes McKinney
- π "Flexible Imputation of Missing Data" by Stef van Buuren
- π "Think Stats: Exploratory Data Analysis in Python" by Allen B. Downey
- π "SQL Notes for Professionals"
- π "Mathematics for Machine Learning" by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
- π "Matrix Analysis" by Joel A. Tropp
- π "Linear Algebra Done Wrong" by Sergei Treil
- π "Linear Algebra Done Right" by Sheldon Axler
- π "Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning" by Jean Gallier and Jocelyn Quaintance
- π "Linear Algebra for Data Science with examples in R" by Shaina Race Bennett
- π "Probability and Statistics - The Science of Uncertainty" by Michael J. Evans and Jeffrey S. Rosenthal
- π "Probability in High Dimensions" by Joel A. Tropp
- π "Introduction to Probability" by Joseph K. Blitzstein and Jessica Hwang
- π "A History of the Central Limit Theorem From Classical to Modern Probability Theory" by Hans Fischer
- π "Think Bayes: Bayesian Statistics Made Simple" by Allen B. Downey
- π "Introduction to Bayesian Statistics" by Brendon J. Brewer
- π "learning statistics with jamovi" by Danielle J. Navarro and David R. Foxcroft
- π "The Truth about Linear Regression" by Cosma Rohilla Shalizi
- π "Lecture notes on Ridge regression" by Wessel N. van Wieringen
- π "An Introduction to Statistical Learning with Applications in Python" by James, Witten, Hastie, Tibshirani, y Taylor
- π "The Elements of Statistical Learning" by Hastie, Tibshirani y Friedman
- π "Computer Age Statistical Inference: Algorithms, Evidence and Data Science" by Bradley Efron and Trevor Hastie
- π "Pattern Recognition and Machine Learning" by Christopher M. Bishop
- π "Probabilistic Machine Learning: An Introduction" by Kevin Patrick Murphy
- π "Probabilistic Machine Learning: Advanced Topics" by Kevin Patrick Murphy
- π "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David
- π "Foundations of Machine Learning" by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
- π "Gaussian Processes for Machine Learning" by Carl Edward Rasmussen and Christopher K. I. Williams
- π "Information Theory, Inference, and Learning Algorithms" by David J. C. MacKay
- π "Mathematical Analysis of Machine Learning Algorithms" by Tong Zhang
- π "A Comprehensive Guide to Machine Learning" by Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang, Jennifer Listgarten, and Anant Sahai
- π "A Course in Machine Learning" by Hal DaumΓ© III
- π "Machine Learning - A First Course for Engineers and Scientists" by Andreas Lindholm, Niklas WahlstrΓΆm, Fredrik Lindsten, and Thomas B. SchΓΆn
- π "Automated Machine Learning: Methods, Systems, Challenges" by Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren
- π "Statistics and Machine Learning in Python" by Edouard Duchesnay, Tommy LΓΆfstedt, and Feki Younes
- π "Bayesian Reasoning and Machine Learning" by David Barber
- π "R for Data Science" by by Hadley Wickham, Mine Γetinkaya-Rundel, and Garrett Grolemund
- π "Python Data Science Handbook" by Jake VanderPlas
- π "A Guide on Data Analysis" by Mike Nguyen
- π "A Brief Introduction to Machine Learning for Engineers" by Osvaldo Simeone
- π "Machine Learning: The Basics" by Alexander Jung
- π "Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning" by Sebastian Raschka
- π "Hyper-Parameter Optimization: A Review of Algorithms and Applications" by Tong Yu and Hong Zhu
- π "How to avoid machine learning pitfalls: a guide for academic researchers" by Michael A. Lones
- π "Online Learning: A Comprehensive Survey" by Steven C. H. Hoi, Doyen Sahoo, Jing Lu and Peilin Zhao
- π "The Matrix Calculus You Need For Deep Learning" by Terence Parr, and Jeremy Howard
- π "The Modern Mathematics of Deep Learning" by Julius Berner, Philipp Grohs, Gitta Kutyniok, and Philipp Petersen
- π "The Principles of Deep Learning Theory" by Daniel A. Roberts, Sho Yaida, and Boris Hanin
- π "Machine learning with neural networks" by Bernhard Mehlig
- π "Dive into Deep Learning" by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
- π "Deep Learning on Graphs" by Yao Ma and Jiliang Tang
- π "Physics-based Deep Learning" by Nils Thuerey, Philipp Holl, Maximilian Mueller, Patrick Schnell, Felix Trost, and Kiwon Um
- π "The Little Book of Deep Learning" by FranΓ§ois Fleuret
- π "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
- π "Multi-Agent Reinforcement Learning" by Stefano V. Albrecht, Filippos Christianos, and Lukas SchΓ€fer
- π "Recommender Systems" by Linyuan LΓΌ, Matus Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, and Tao Zhou
- π "Thatβs weird! Anomaly detection using R" by Rob J. Hyndman
- π "Computer Vision: Models, Learning, and Inference" by Simon J. D. Prince
- π "Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition" by Dan Jurafsky and James H. Martin
- π "Text Mining with R: A Tidy Approach" by Julia Silge and David Robinson
- π "Large Language Models" by Michael R. Douglas
- π "Causal Machine Learning: A Survey and Open Problems" by Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, and Ricardo Silva
- π "Recent Developments in Causal Inference and Machine Learning" by Jennie E. Brand, Xiang Zhou, and Yu Xie
- π "Causal Factor Investing" by Marcos M. LΓ³pez de Prado
- π "A Tutorial on Conformal Prediction" by Glenn Shafer and Vladimir Vovk
- π "Conformal Prediction: a Unified Review of Theory and New Challenges" by Matteo Fontana, Gianluca Zeni and Simone Vantini
- π "A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification" by Anastasios N. Angelopoulos and Stephen Bates
- π "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Max Kuhn and Kjell Johnson
- π "Forecasting: Principles and Practice" by Rob J Hyndman and George Athanasopoulos
- π "Forecasting: theory and practice" by Fotios Petropoulos et al.
- π "Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices" by Hansika Hewamalagea, Klaus Ackermannb, and Christoph Bergmeir
- π "A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting" by Aryan Jadon, Avinash Patil and Shruti Jadon
- π "Time Series Analysis" by Alexander Aue
- π "Demand Forecasting for Executives and Professionals" by Stephan Kolassa, Bahman Rostami-Tabar, and Enno Siemsen
- π "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable" by Christoph Molnar
Note: All arXiv papers are inherently free, and all books have been made freely available by their respective authors/publishers.