(Key 📙 pdf file 🌍 html book)

• 🌍 "Python for Data Analysis (3rd Edition)" by Wes McKinney
• 🌍 "Flexible Imputation of Missing Data" by Stef van Buuren
• 🌍 "Fundamentals of Data Visualization" by Claus O. Wilke
• 🌍 "R Graphics Cookbook" by Winston Chang
• 🌍 "Modern Data Visualization with R" by Robert Kabacoff
• 📙 "Think Stats: Exploratory Data Analysis in Python" by Allen B. Downey
• 📙 "SQL Notes for Professionals"

• 📙 "Mathematics for Machine Learning" by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
• 📙 "Matrix Analysis" by Joel A. Tropp
• 📙 "Linear Algebra Done Wrong" by Sergei Treil
• 📙 "Linear Algebra Done Right" by Sheldon Axler
• 📙 "Linear Algebra, Theory And Applications" by Kenneth Kuttler
• 📙 "Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning" by Jean Gallier and Jocelyn Quaintance
• 📙 "The Matrix Cookbook" by Kaare Brandt Petersen, and Michael Syskind Pedersen
• 📙 "Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares" by Stephen Boyd and Lieven Vandenberghe
• 📙 "Linear Algebra for Data Science" by Wanmo Kang, and Kyunghyun Cho
• 🌍 "Linear Algebra for Data Science with examples in R" by Shaina Race Bennett

• 📙 "Probability and Statistics - The Science of Uncertainty" by Michael J. Evans and Jeffrey S. Rosenthal
• 📙 "Probability in High Dimensions" by Joel A. Tropp
• 📙 "Introduction to Probability" by Joseph K. Blitzstein and Jessica Hwang
• 📙 "A History of the Central Limit Theorem From Classical to Modern Probability Theory" by Hans Fischer
• 📙 "Think Bayes: Bayesian Statistics Made Simple" by Allen B. Downey
• 📙 "Introduction to Bayesian Statistics" by Brendon J. Brewer
• 📙 "learning statistics with jamovi" by Danielle J. Navarro and David R. Foxcroft
• 📙 "Bayesian Data Analysis" by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin
• 📙 "Compendium of Common Probability Distributions" by Michael P. McLaughlin
• 📙 "Probability and Statistics for Data Science" by Carlos Fernandez-Granda
• 📙 "Statistical Modeling: An Excursion Through 14 Topics" by Reinhard Furrer

• 📙 "The Truth about Linear Regression" by Cosma Rohilla Shalizi
• 📙 "Lecture notes on Ridge regression" by Wessel N. van Wieringen
• 📙 "Linear Model and Extensions" by Peng Ding
• 📙 "Regression and Other Stories" by Andrew Gelman, Jennifer Hill, and Aki Vehtari
• 🌍 "Analysing Data using Linear Models" by Stéphanie M. van den Berg

• 📙 "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe
• 📙 "An introduction to optimization on smooth manifolds" by Nicolas Boumal

• 📙 "An Introduction to Statistical Learning with Applications in Python" by James, Witten, Hastie, Tibshirani, and Taylor
• 📙 "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman
• 📙 "Computer Age Statistical Inference: Algorithms, Evidence and Data Science" by Bradley Efron and Trevor Hastie
• 📙 "Pattern Recognition and Machine Learning" by Christopher M. Bishop
• 📙 "Probabilistic Machine Learning: An Introduction" by Kevin Patrick Murphy
• 📙 "Probabilistic Machine Learning: Advanced Topics" by Kevin Patrick Murphy
• 📙 "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz and Shai Ben-David
• 📙 "Foundations of Machine Learning" by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
• 📙 "Gaussian Processes for Machine Learning" by Carl Edward Rasmussen and Christopher K. I. Williams
• 📙 "Information Theory, Inference, and Learning Algorithms" by David J. C. MacKay
• 📙 "Mathematical Analysis of Machine Learning Algorithms" by Tong Zhang
• 📙 "A Comprehensive Guide to Machine Learning" by Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang, Jennifer Listgarten, and Anant Sahai
• 📙 "A Course in Machine Learning" by Hal Daumé III
• 📙 "Machine Learning - A First Course for Engineers and Scientists" by Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön
• 📙 "Automated Machine Learning: Methods, Systems, Challenges" by Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren
• 📙 "Statistics and Machine Learning in Python" by Edouard Duchesnay, Tommy Löfstedt, and Feki Younes
• 📙 "Bayesian Reasoning and Machine Learning" by David Barber
• 📙 "Boosting: Foundations and Algorithms" by Robert E. Schapire, and Yoav Freund
• 📙 "Algorithms for Decision Making" by Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray
• 📙 "Introduction to Algorithmic Marketing" by Ilya Katsov
• 📙 "Applied Data Science" by Ian Langmore, and Daniel Krasner
• 📙 "CS229 Lecture Notes" by Andrew Ng, and Tengyu Ma
• 📙 "Random Matrix Methods for Machine Learning" by Romain Couillet, and Zhenyu Liao
• 📙 "The Orange Book of Machine Learning" by Carl McBride Ellis
• 📙 "Learning Theory from First Principles" by Francis Bach
• 📙 "Advanced Data Analysis from an Elementary Point of View" by Cosma Rohilla Shalizi
• 📙 "Machine Learning" by Tom Mitchell
• 🌍 "Supervised Machine Learning for Science" by Christoph Molnar, and Timo Freiesleben
• 🌍 "Python Data Science Handbook" by Jake VanderPlas
• 🌍 "A Guide on Data Analysis" by Mike Nguyen
• 🌍 "Applied Machine Learning for Tabular Data" by Max Kuhn, and Kjell Johnson

• 📙 "A Brief Introduction to Machine Learning for Engineers" by Osvaldo Simeone
• 📙 "Machine Learning: The Basics" by Alexander Jung
• 📙 "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
• 📙 "Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning" by Sebastian Raschka
• 📙 "Hyper-Parameter Optimization: A Review of Algorithms and Applications" by Tong Yu and Hong Zhu
• 📙 "How to avoid machine learning pitfalls: a guide for academic researchers" by Michael A. Lones
• 📙 "Online Learning: A Comprehensive Survey" by Steven C. H. Hoi, Doyen Sahoo, Jing Lu and Peilin Zhao

• 🌍 "Big Book of R" by Oscar Baruffa
• 🌍 "Hands-On Programming with R" by Garrett Grolemund
• 🌍 "Hands-On Machine Learning with R" by Bradley Boehmke and Brandon Greenwell
• 🌍 "Linear Algebra for Data Science with examples in R" by Shaina Race Bennett
• 📙 "Deep R Programming" by Marek Gagolewski
• 🌍 "Efficient R programming" by Colin Gillespie, and Robin Lovelace
• 🌍 "R for Data Science" by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund
• 🌍 "Advanced R" by Hadley Wickham
• 🌍 "The Epidemiologist R Handbook" editor Neale Batra
• 🌍 "Practical Statistics in Medicine with R" by Konstantinos I. Bougioukas
• 🌍 "Text Mining with R: A Tidy Approach" by Julia Silge and David Robinson
• 🌍 "Spatial Statistics for Data Science: Theory and Practice with R" by Paula Moraga
• 🌍 "R Graphics Cookbook" by Winston Chang
• 🌍 "Toolbox for Social Scientists and Policy Analysts: Applied Predictive Analytics with Machine Learning and R" by Yigit Aydede
• 🌍 "Fundamentos de ciencia de datos con R" by Gema Fernández-Avilés Calderón y José-María Montero
• 🌍 "Modern Data Visualization with R" by Robert Kabacoff
• 🌍 "Introduction to Econometrics with R" by Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer
• 📙 "The R Inferno" by Patrick Burns

• 📙 "The Matrix Calculus You Need For Deep Learning" by Terence Parr, and Jeremy Howard
• 📙 "The Modern Mathematics of Deep Learning" by Julius Berner, Philipp Grohs, Gitta Kutyniok, and Philipp Petersen
• 📙 "The Principles of Deep Learning Theory" by Daniel A. Roberts, Sho Yaida, and Boris Hanin
• 📙 "Machine learning with neural networks" by Bernhard Mehlig
• 📙 "Dive into Deep Learning" by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
• 📙 "Deep Learning on Graphs" by Yao Ma and Jiliang Tang
• 📙 "Physics-based Deep Learning" by Nils Thuerey, Philipp Holl, Maximilian Mueller, Patrick Schnell, Felix Trost, and Kiwon Um
• 📙 "Understanding Deep Learning" by Simon J. D. Prince
• 📙 "The Little Book of Deep Learning" by François Fleuret
• 📙 "Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory" by Arnulf Jentzen, Benno Kuckuck, and Philippe von Wurstemberger
• 📙 "Theory of Deep Learning" by Sanjeev Arora, et al.
• 📙 "What are embeddings" by Vicki Boykis
• 🌍 "Deep Learning: Foundations and Concepts" by Christopher M. Bishop and Hugh Bishop

• 📙 "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
• 📙 "Multi-Agent Reinforcement Learning" by Stefano V. Albrecht, Filippos Christianos, and Lukas Schäfer
• 📙 "Distributional Reinforcement Learning" by Marc G. Bellemare, Will Dabney, and Mark Rowland

• 📙 "Recommender Systems" by Linyuan Lü, Matus Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, and Tao Zhou
• 📙 "Recommender Systems: A Primer" Pablo Castells, and Dietmar Jannach

• 🌍 "That’s weird! Anomaly detection using R" by Rob J. Hyndman

• 📙 "Computer Vision: Models, Learning, and Inference" by Simon J. D. Prince
• 📙 "Computer Vision: Algorithms and Applications" (1st Ed) by Richard Szeliski

• 📙 "Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition" by Dan Jurafsky and James H. Martin
• 📙 "Large Language Models" by Michael R. Douglas
• 📙 "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
• 📙 "Formal Aspects of Language Modeling" by Ryan Cotterell, Anej Svete, Clara Meister, Tianyu Liu, and Li Du
• 📙 "A Survey of Large Language Models" by Wayne Xin Zhao, et al.
• 📙 "Foundation Models for Natural Language Processing" by Gerhard Paaß, and Sven Giesselbach
• 🌍 "Text Mining with R: A Tidy Approach" by Julia Silge and David Robinson

• 📙 "Causal Machine Learning: A Survey and Open Problems" by Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, and Ricardo Silva
• 📙 "Recent Developments in Causal Inference and Machine Learning" by Jennie E. Brand, Xiang Zhou, and Yu Xie
• 📙 "A First Course in Causal Inference" by Peng Ding
• 📙 "Causal Factor Investing" by Marcos M. López de Prado
• 📙 "Survey and Evaluation of Causal Discovery Methods for Time Series" by Charles K. Assaad, Emilie Devijver, and Eric Gaussier
• 📙 "Applied Causal Inference Powered by ML and AI" by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis
• 🌍 "Applied Causal Inference" by Uday Kamath, Kenneth Graham, and Mitchell Naylor
• 🌍 "Causal Inference for The Brave and True" by Matheus Facure Alves

• 📙 "A Tutorial on Conformal Prediction" by Glenn Shafer and Vladimir Vovk
• 📙 "Conformal Prediction: a Unified Review of Theory and New Challenges" by Matteo Fontana, Gianluca Zeni and Simone Vantini
• 📙 "A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification" by Anastasios N. Angelopoulos and Stephen Bates

• 🌍 "Feature Engineering and Selection: A Practical Approach for Predictive Models" by Max Kuhn and Kjell Johnson
• 🌍 "Feature Engineering A-Z" by Emil Hvitfeldt

• 🌍 "Forecasting: Principles and Practice" by Rob J Hyndman and George Athanasopoulos
• 📙 "Forecasting: theory and practice" by Fotios Petropoulos et al.
• 🌍 "Forecasting: theory and practice" (Online version) Editors: Fotios Petropoulos, Yanfei Kang, and Feng Li
• 📙 "Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices" by Hansika Hewamalagea, Klaus Ackermannb, and Christoph Bergmeir
• 📙 "A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting" by Aryan Jadon, Avinash Patil and Shruti Jadon
• 📙 "Time Series Analysis" by Alexander Aue
• 📙 "Time Series for Macroeconomics and Finance" by John H. Cochrane
• 🌍 "Demand Forecasting for Executives and Professionals" by Stephan Kolassa, Bahman Rostami-Tabar, and Enno Siemsen

• 🌍 "Interpretable Machine Learning: A Guide for Making Black Box Models Explainable" by Christoph Molnar
• 🌍 "Explanatory Model Analysis" by Przemyslaw Biecek and Tomasz Burzykowski

_{Note: All arXiv papers are inherently free, and all books have been made freely available by their respective authors/publishers.}