Key:
- π pdf file
- π html book
- EDA, visualization, and data cleaning (8)
- Mathematics for ML (13)
- Statistics and probability (16)
- Optimization (7)
- Linear regression (6)
- Machine learning (48)
- R realted (18)
- Feature engineering (2)
- Explainability/interpretability (6)
- Deep learning / neural networks (17)
- Reinforcement learning (8)
- Recommender systems (2)
- Anomaly detection (1)
- Computer vision (3)
- Natural language processing (NLP) and large language models (LLM) (13)
- Causal inference (13)
- Conformal prediction (5)
- Time series: Forecasting (6)
Total number of books: 193
Note: All books listed here have been made freely available by their respective authors/publishers, and all arXiv papers are inherently free.
- π "Python for Data Analysis (3rd Edition)" by Wes McKinney
- π "Flexible Imputation of Missing Data" by Stef van Buuren
- π "Fundamentals of Data Visualization" by Claus O. Wilke
- π "R Graphics Cookbook" by Winston Chang
- π "Modern Data Visualization with R" by Robert Kabacoff
- π "Data Cleaning and Machine Learning: A Systematic Literature Review" by Pierre-Olivier CΓ΄tΓ©, Amin Nikanjam, Nafisa Ahmed, Dmytro Humeniuk, and Foutse Khomh
- 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 "The Matrix Calculus You Need For Deep Learning" by Terence Parr, and Jeremy Howard
- π "Matrix Analysis" by Joel A. Tropp
- π "Linear Algebra Done Wrong" by Sergei Treil
- π "Linear Algebra Done Right" by Sheldon Axler
- π "Linear Algebra, Theory And Applications" by Kenneth Kuttler
- π "The Matrix Cookbook" by Kaare Brandt Petersen, and Michael Syskind Pedersen
- π "Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares" by Stephen Boyd and Lieven Vandenberghe
- π "Linear Algebra for Data Science" by Wanmo Kang, and Kyunghyun Cho
- π "Linear Algebra for Computer Vision, Robotics, and Machine Learning" by Jean Gallier and Jocelyn Quaintance
- π "Matrix Calculus (for Machine Learning and Beyond)" by Paige Bright, Alan Edelman, Steven G. Johnson
- π "Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning" by Jean Gallier and Jocelyn Quaintance
- π "Foundations of Linear Algebra for Data Science" by Wanmo Kang and Kyunghyun Cho
- 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 "Bayesian Data Analysis" by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin
- π "Compendium of Common Probability Distributions" by Michael P. McLaughlin
- π "Probability and Statistics for Data Science" by Carlos Fernandez-Granda
- π "Statistical Modeling: An Excursion Through 14 Topics" by Reinhard Furrer
- π "Robust Statistics" by David J. Olive
- π "CS109 Probability for Computer scientists" by Chris Piech
- π "Odds & Ends: Introducing Probability & Decision with a Visual Emphasis" by Jonathan Weisberg
- π "Grinstead and Snellβs Introduction to Probability" by Peter G. Doyle
- π "High-Dimensional Probability: An Introduction with Applications in Data Science" by Roman Vershynin
- π "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe
- π "An introduction to optimization on smooth manifolds" by Nicolas Boumal
- π "Lecture Notes: Optimization for Machine Learning" by Elad Hazan
- π "A Modern Approach to Teaching an Introduction to Optimization" by Warren B. Powell
- π "Bayesian Optimization" by Roman Garnett
- π "Fundamentals of Optimization Theory With Applications to Machine Learning" by Jean Gallier and Jocelyn Quaintance
- π "Algorithms for Optimization" by Mykel J. Kochenderfer, and Tim A. Wheeler
- π "The Truth about Linear Regression" by Cosma Rohilla Shalizi
- π "Lecture notes on Ridge regression" by Wessel N. van Wieringen
- π "Linear Model and Extensions" by Peng Ding
- π "Regression and Other Stories" by Andrew Gelman, Jennifer Hill, and Aki Vehtari
- π "Lecture Notes on High Dimensional Linear Regression" by Alberto Quaini
- π "Analysing Data using Linear Models" by StΓ©phanie M. van den Berg
- π "An Introduction to Statistical Learning with Applications in Python" by James, Witten, Hastie, Tibshirani, and Taylor
- π "The Elements of Statistical Learning" by Hastie, Tibshirani, and 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 "Boosting: Foundations and Algorithms" by Robert E. Schapire, and Yoav Freund
- π "Algorithms for Decision Making" by Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray
- π "Introduction to Algorithmic Marketing" by Ilya Katsov
- π "Applied Data Science" by Ian Langmore, and Daniel Krasner
- π "CS229 Lecture Notes" by Andrew Ng, and Tengyu Ma
- π "Random Matrix Methods for Machine Learning" by Romain Couillet, and Zhenyu Liao
- π "The Orange Book of Machine Learning" by Carl McBride Ellis
- π "Learning Theory from First Principles" by Francis Bach
- π "Advanced Data Analysis from an Elementary Point of View" by Cosma Rohilla Shalizi
- π "Machine Learning" by Tom Mitchell
- π "Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide" Eds.: Eva Bartz, Thomas Bartz-Beielstein, Martin Zaefferer, and Olaf Mersmann
- π "Foundations of Data Science" by Avrim Blum, John Hopcroft, and Ravindran Kannan
- π "Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent Systems" by VΔ³ay Janapa Reddi
- π "Concise Machine Learning" by Jonathan Richard Shewchuk
- π "Data Science and Machine Learning: Mathematical and Statistical Methods" by Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, and Radislav Vaisman
- π "Probabilistic Artificial Intelligence" by Andreas Krause, and Jonas HΓΌbotter
- π "A Brief Introduction to Machine Learning for Engineers" by Osvaldo Simeone
- π "Machine Learning: The Basics" by Alexander Jung
- π "A high-bias, low-variance introduction to Machine Learning for physicists" by Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab
- 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 "Introduction to Machine Learning" by Laurent Younes
- π "Supervised Machine Learning for Science" by Christoph Molnar, and Timo Freiesleben
- π "Python Data Science Handbook" by Jake VanderPlas
- π "A Guide on Data Analysis" by Mike Nguyen
- π "Applied Machine Learning for Tabular Data" by Max Kuhn, and Kjell Johnson
- π "Applied Machine Learning in Python: a Hands-on Guide with Code" by Michael J. Pyrcz
- π "Learning Data Science" by Sam Lau, Joey Gonzalez, and Deb Nolan
- π "Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making" by Bin Yu, and Rebecca L. Barter
R realted
- π "Big Book of R" by Oscar Baruffa
- π "Hands-On Programming with R" by Garrett Grolemund
- π "Hands-On Machine Learning with R" by Bradley Boehmke and Brandon Greenwell
- π "Linear Algebra for Data Science with examples in R" by Shaina Race Bennett
- π "Deep R Programming" by Marek Gagolewski
- π "Efficient R programming" by Colin Gillespie, and Robin Lovelace
- π "R for Data Science" by Hadley Wickham, Mine Γetinkaya-Rundel, and Garrett Grolemund
- π "Advanced R" by Hadley Wickham
- π "The Epidemiologist R Handbook" editor Neale Batra
- π "Practical Statistics in Medicine with R" by Konstantinos I. Bougioukas
- π "Text Mining with R: A Tidy Approach" by Julia Silge and David Robinson
- π "Spatial Statistics for Data Science: Theory and Practice with R" by Paula Moraga
- π "R Graphics Cookbook" by Winston Chang
- π "Toolbox for Social Scientists and Policy Analysts: Applied Predictive Analytics with Machine Learning and R" by Yigit Aydede
- π "Fundamentos de ciencia de datos con R" by Gema FernΓ‘ndez-AvilΓ©s CalderΓ³n y JosΓ©-MarΓa Montero
- π "Modern Data Visualization with R" by Robert Kabacoff
- π "Introduction to Econometrics with R" by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer
- π "The R Inferno" by Patrick Burns
- π "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Max Kuhn and Kjell Johnson
- π "Feature Engineering A-Z" by Emil Hvitfeldt
- π "Trustworthy Machine Learning" by Kush R. Varshney
- π "Trustworthy Machine Learning: Theory, Applications, Intuitions" by BΓ‘lint MucsΓ‘nyi, Michael Kirchhof, Elisa Nguyen, Alexander Rubinstein, and Seong Joon Oh
- π "A Comprehensive Guide to Explainable AI: From Classical Models to LLMs" by Weiche Hsieh, et al.
- π "Interpretable Machine Learning in Physics: A Review" by Wetzel, Ha, Iten, Klopotek, and Liu
- π "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable" by Christoph Molnar
- π "Explanatory Model Analysis" by Przemyslaw Biecek and Tomasz Burzykowski
- π "The Modern Mathematics of Deep Learning" by Julius Berner, Philipp Grohs, Gitta Kutyniok, and Philipp Petersen
- π "Mathematics of Neural Networks" by Bart M.N. Smets
- 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 "Understanding Deep Learning" by Simon J. D. Prince
- π "The Little Book of Deep Learning" by FranΓ§ois Fleuret
- π "Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory" by Arnulf Jentzen, Benno Kuckuck, and Philippe von Wurstemberger
- π "Mathematical theory of deep learning" by Philipp Petersen, and Jakob Zech
- π "Theory of Deep Learning" by Sanjeev Arora, et al.
- π "Loss Functions and Metrics in Deep Learning" by Juan Terven, Diana M. Cordova-Esparza, Alfonso Ramirez-Pedraza, Edgar A. Chavez-Urbiola, and Julio A. Romero-Gonzalez
- π "Understanding Deep Learning" by Chitta Ranjan
- π "Deep Learning: Foundations and Concepts" by Christopher M. Bishop and Hugh Bishop
- π "Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD" by Jeremy Howard, and Sylvain Gugger
- π "Deep Learning with Python, Third Edition" by FranΓ§ois Chollet, and Matthew Watson
- 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 "Distributional Reinforcement Learning" by Marc G. Bellemare, Will Dabney, and Mark Rowland
- π "Mathematical Foundations of Reinforcement Learning" by Shiyu Zhao
- π "Reinforcement Learning: Foundations" by Shie Mannor, Yishay Mansour, and Aviv Tamar
- π "Reinforcement Learning: An Overview" by Kevin Murphy
- π "An Introduction to Deep Reinforcement Learning" by Vincent Francois-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, and Joelle Pineau
- π "The Mathematics of Reinforcement Learning" by Leif DΓΆring
- π "Recommender Systems" by Linyuan LΓΌ, Matus Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, and Tao Zhou
- π "Recommender Systems: A Primer" Pablo Castells, and Dietmar Jannach
- π "Thatβs weird! Anomaly detection using R" by Rob J. Hyndman
- π "Computer Vision: Models, Learning, and Inference" by Simon J. D. Prince
- π "Computer Vision: Algorithms and Applications" (1st Ed) by Richard Szeliski
- π "Foundations of Computer Vision" by Antonio Torralba, Phillip Isola, and William Freeman
- π "Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition" by Dan Jurafsky and James H. Martin
- π "Large Language Models" by Michael R. Douglas
- π "Graph Neural Networks for Natural Language Processing: A Survey" by Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, and Bo Long
- π "Formal Aspects of Language Modeling" by Ryan Cotterell, Anej Svete, Clara Meister, Tianyu Liu, and Li Du
- π "Foundation Models for Natural Language Processing" by Gerhard PaaΓ, and Sven Giesselbach
- π "What are embeddings" by Vicki Boykis
- π "A Survey of Large Language Models" by Wayne Xin Zhao, et al.
- π "Large Language Models: A Survey" by Shervin Minaee, et al.
- π "A Comprehensive Overview of Large Language Models" by Humza Naveed, et al.
- π "A Survey on Large Language Models with some Insights on their Capabilities and Limitations" by Andrea Matarazzo, and Riccardo Torlone
- π "Foundations of Large Language Models" by Tong Xiao, and Jingbo Zhu
- π "Text Mining with R: A Tidy Approach" by Julia Silge and David Robinson
- π "Natural Language Processing with Python - Analyzing Text with the Natural Language Toolkit" by Steven Bird, Ewan Klein, and Edward Loper
- 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 "A First Course in Causal Inference" by Peng Ding
- π "Causal Factor Investing" by Marcos M. LΓ³pez de Prado
- π "Survey and Evaluation of Causal Discovery Methods for Time Series" by Charles K. Assaad, Emilie Devijver, and Eric Gaussier
- π "Applied Causal Inference Powered by ML and AI" by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis
- π "Causal Inference: A Statistical Learning Approach" by Stefan Wager
- π "Probability and Causality. Conditional and Average Total Effects" by Rolf Steyer
- π "What If" by Miguel A. HernΓ‘n, and James M. Robins
- π "Causal Artificial Intelligence" by Elias Bareinboim
- π "Applied Causal Inference" by Uday Kamath, Kenneth Graham, and Mitchell Naylor
- π "Causal Inference for The Brave and True" by Matheus Facure Alves
- π "How to Interpret Statistical Models Using marginaleffects for R and Python" by Vincent Arel-Bundock, Noah Greifer, and Andrew Heiss
- 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 "Theoretical Foundations of Conformal Prediction" by Anastasios N. Angelopoulos, Rina Foygel Barber, and Stephen Bates
- π "Machine Learning Learning Beyond Point Predictions: Uncertainty Quantification" by Rafael Izbicki
- π "Forecasting: Principles and Practice" by Rob J. Hyndman and George Athanasopoulos
- π "Forecasting: Principles and Practice, the Pythonic Way" by Rob J. Hyndman, George Athanasopoulos, Azul Garza, Cristian Challu, Max Mergenthaler, and Kin G. Olivares
- π "Forecasting: theory and practice" by Fotios Petropoulos et al.
- π "Forecasting: theory and practice" (Online version) Editors: Fotios Petropoulos, Yanfei Kang, and Feng Li
- π "Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices" by Hansika Hewamalagea, Klaus Ackermannb, and Christoph Bergmeir
- π "Time Series Analysis" by Alexander Aue
- π "Time Series for Macroeconomics and Finance" by John H. Cochrane
- π "Demand Forecasting for Executives and Professionals" by Stephan Kolassa, Bahman Rostami-Tabar, and Enno Siemsen