README.md

BookList For AI,ML,DL and Data Eng

---

                    Reading List

---

Starters

• Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow : Aurélien Géron
• Python for Data Analysis : Wes McKinney

Data Cleaning

• Bad Data : Q. Ethan McCallum
• Best Practices in Data Cleaning : Jason W. Osborne

Free Books Taken From Here

---

Artificial Intelligence

• Artificial Intelligence for a Better Future: An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies - Bernd Carsten Stahl (PDF)
• Artificial Intelligence: Foundations of Computational Agents (2010), 1st Edition - David L. Poole, Alan K. Mackworth @ Cambridge University Press (HTML)
• Artificial Intelligence: Foundations of Computational Agents (2017), 2nd Edition - David L. Poole, Alan K. Mackworth @ Cambridge University Press (HTML, Slides)
• Getting Started with Artificial Intelligence , 2nd Edition - Tom Markiewicz, Josh Zheng (PDF)
• Graph Representational Learning Book - William L. Hamilton (HTML, PDF)
• Introduction to Autonomous Robots - Nikolaus Correll (PDF)
• On the Path to AI: Law’s prophecies and the conceptual foundations of the machine learning age - Thomas D. Grant, Damon J. Wischik (PDF)
• Probabilistic Programming & Bayesian Methods for Hackers - Cam Davidson-Pilon (HTML, Jupyter Notebook)
• The Quest for Artificial Intelligence: A History of Ideas and Achievements - Nils J. Nilsson (PDF)

Computer Vision

• Computer Vision - Dana Ballard, Chris Brown
• Computer Vision: Algorithms and Applications - Richard Szeliski
• Computer Vision: Models, Learning, and Inference - Simon J.D. Prince
• DALLE-E 2 prompt book - Dallery.Gallery, Guy Parson(PDF)
• Programming Computer Vision with Python - Jan Erik Solem

Data Science

• A Programmer's Guide to Data Mining - Ron Zacharski (Draft)
• Data Jujitsu: The Art of Turning Data into Product (email address requested, not required)
• Data Mining Algorithms In R - Wikibooks
• Elements of Data Science - Allen B. Downey
• Fundamentals of Data Visualization - Claus O. Wilke (HTML)
• Hands-On Data Visualization - Jack Dougherty, Ilya Ilyankou (HTML)
• Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users (PDF)
• Introduction to Data Science - Jeffrey Stanton
• Mining of Massive Datasets - Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman (PDF)
• Probability and Statistics with Examples using R - Siva Athreya, Deepayan Sarkar, Steve Tanner (HTML) (:construction: in process)
• School of Data Handbook
• Statistical inference for data science - Brian Caffo
• The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python codes) - Pulkit Sharma
• Theory and Applications for Advanced Text Mining

Database

• Database Design – 2nd Edition - Adrienne Watt, Nelson Eng @ BCcampus Open Pressbooks (HTML, PDF, EPUB, Kindle)
• Database Design Succinctly - Joseph D. Booth (HTML, PDF, EPUB, MOBI)
• Database Explorations - C.J. Date, Hugh Darwen (PDF)
• Database Fundamentals - Neeraj Sharma et al. (PDF)
• Databases, Types, and The Relational Model: The Third Manifesto - C.J. Date, Hugh Darwen (PDF)
• Foundations of Databases
• Readings in Database Systems, 5th Ed.
• Temporal Database Management - Christian S. Jensen
• The Theory of Relational Databases

Machine Learning

• A Brief Introduction to Machine Learning for Engineers - Osvaldo Simeone (PDF)
• A Brief Introduction to Neural Networks
• A Comprehensive Guide to Machine Learning - Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang (PDF)
• A Course in Machine Learning (PDF)
• A First Encounter with Machine Learning - Max Welling (PDF) (:card_file_box: archived)
• A Selective Overview of Deep Learning - Fan, Ma, Zhong (PDF)
• Algorithms for Reinforcement Learning - Csaba Szepesvári (PDF)
• An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (PDF)
• Approaching Almost Any Machine Learning Problem - Abhishek Thakur (PDF)
• Bayesian Reasoning and Machine Learning
• Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville
• Deep Learning for Coders with Fastai and PyTorch - Jeremy Howard, Sylvain Gugger (Jupyter Notebooks)
• Dive into Deep Learning
• Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises - James L. McClelland
• Foundations of Machine Learning, Second Edition - Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
• Free and Open Machine Learning - Maikel Mardjan (HTML)
• Gaussian Processes for Machine Learning - Carl Edward Rasmussen, Christopher K.I. Williams
• IBM Machine Learning for Dummies - Judith Hurwitz, Daniel Kirsch
• Information Theory, Inference, and Learning Algorithms - David J.C. MacKay
• Interpretable Machine Learning - Christoph Molnar
• Introduction to CNTK Succinctly - James McCaffrey
• Introduction to Machine Learning - Amnon Shashua
• Keras Succinctly - James McCaffrey
• Learn Tensorflow - Jupyter Notebooks
• Learning Deep Architectures for AI - Yoshua Bengio (PDF)
• Machine Learning
• Machine Learning for Data Streams - Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
• Machine Learning from Scratch - Danny Friedman (HTML, PDF, Jupyter Book)
• Machine Learning, Neural and Statistical Classification - D. Michie, D.J. Spiegelhalter, C.C. Taylor
• Machine Learning with Python - Tutorials Point (HTML, PDF)
• Mathematics for Machine Learning - Garrett Thomas (PDF)
• Mathematics for Machine Learning - Marc Peter Deisenroth, A Aldo Faisal, Cheng Soon Ong
• Neural Networks and Deep Learning
• Practitioners guide to MLOps - Khalid Samala, Jarek Kazmierczak, Donna Schut (PDF)
• Probabilistic Machine Learning - An Introduction - Kevin P. Murphy (PDF)
• Probabilistic Models in the Study of Language (Draft, with R code)
• Python Machine Learning Projects - Lisa Tagliaferri, Brian Boucheron, Michelle Morales, Ellie Birkbeck, Alvin Wan (PDF, EPUB, Kindle)
• Reinforcement Learning: An Introduction - Richard S. Sutton, Andrew G. Barto (PDF)
• Speech and Language Processing (3rd Edition Draft) - Daniel Jurafsky, James H. Martin (PDF)
• The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
• The LION Way: Machine Learning plus Intelligent Optimization - Roberto Battiti, Mauro Brunato (PDF)
• The Mechanics of Machine Learning - Terence Parr, Jeremy Howard
• The Python Game Book - Horst Jens (:card_file_box: archived)
• Top 10 Machine Learning Algorithms Every Engineer Should Know - Binny Mathews, Omair Aasim
• Understanding Machine Learning: From Theory to Algorithms - Shai Shalev-Shwartz, Shai Ben-David

Mathematics

• A Computational Introduction to Number Theory and Algebra - Victor Shoup
• A Computational Logic (1979) - Robert S. Boyer, J Strother Moore (PDF)
• A First Course in Complex Analysis - Matthias Beck, Gerald Marchesi, Dennis Pixton, Lucas Sabalka
• A First Course in Linear Algebra - Rob Beezer
• A Friendly Introduction to Mathematical Logic - Christopher C. Leary, Lars Kristiansen
• A Gentle Introduction to the Art of Mathematics - Joseph E. Fields
• A Programmer's Introduction to Mathematics - Jeremy Kun
• Abstract Algebra: Theory and Applications - Tom Judson
• Active Calculus - Matt Boelkins
• Advanced Algebra - Anthony W. Knapp (PDF)
• Algebra: Abstract and Concrete - Frederick Goodman
• Algebra: An Elementary Text-Book, Part I (1904) - G. Chrystal (PDF)
• Algebra: An Elementary Text-Book, Part II (1900) - G. Chrystal (PDF)
• Algebraic Topology - Allen Hatcher (PDF)
• An Infinite Descent into Pure Mathematics - Clive Newstead (PDF)
• An Introduction to the Theory of Numbers - Leo Moser (PDF)
• Analytic Geometry (1922) - Lewis Parker Siceloff, George Wentworth, David Eugene Smith (PDF)
• APEX Calculus - Gregory Hartman, Brian Heinold, Troy Siemers, and Dimplekumar Chalishajar
• Applied Combinatorics - Mitchel T. Keller, William T. Trotter
• Applied Discrete Structures - Alan Doerr, Kenneth Levasseur
• Basic Algebra - Anthony W. Knapp (PDF)
• Basic Analysis: Introduction to Real Analysis - Jiří Lebl
• Basics of Algebra, Topology, and Differential Calculus (PDF)
• Bayesian Methods for Hackers - Cameron Davidson-Pilon
• Beginning and Intermediate Algebra - Tyler Wallace
• Book of Proof - Richard Hammack (PDF)
• Calculus - Gilbert Strang
• Calculus I - Jerrold E. Marsden, Alan Weinstein
• Calculus in Context - James Callahan
• Calculus Made Easy - Silvanus P. Thompson (PDF)
• Calculus Volume 1 - Edwin Herman, Gilbert Strang (PDF)
• Calculus Volume 2 - Edwin Herman, Gilbert Strang (PDF)
• Calculus Volume 3 - Edwin Herman, Gilbert Strang (PDF)
• Category Theory for the Sciences - David I. Spivak (PDF)
• CK-12 Probability and Statistics - Advanced
• CLP-1 Differential Calculus - Joel Feldman, Andrew Rechnitzer, Elyse Yeager
• CLP-2 Integral Calculus - Joel Feldman, Andrew Rechnitzer, Elyse Yeager
• CLP-3 Multivariable Calculus - Joel Feldman, Andrew Rechnitzer, Elyse Yeager
• CLP-4 Vector Calculus - Joel Feldman, Andrew Rechnitzer, Elyse Yeager
• Collaborative Statistics
• College Trigonometry - Carl Stitz, Jeff Zeager (PDF)
• Combinatorics Through Guided Discovery - Kenneth Bogart
• Complex Analysis - George Cain
• Computational and Inferential Thinking. The Foundations of Data Science - Ani Adhikari, John DeNero, David Wagner
• Computational Geometry
• Computational Mathematics with SageMath - Paul Zimmermann, Alexandre Casamayou, Nathann Cohen, Guillaume Connan, et al. (PDF)
• Concepts & Applications of Inferential Statistics
• Convex Optimization - Stephen Boyd, Lieven Vandenberghe
• Coordinate Geometry (1911) - Henry Buchard Fine, Henry Dallas Thompson (PDF)
• Differential Equations - Paul Dawkins (PDF, use download menu to download)
• Differential Equations (1922) - H. B. Phillips (PDF)
• Discrete Mathematics: An Open Introduction - Oscar Levin
• Discrete Mathematics: First and Second Course - Edward A. Bender, S. Gill Williamson
• Elementary Differential Equations - William F. Trench (PDF)
• Elementary Differential Equations (with Boundary Value Problems) - William F. Trench
• Elementary Number Theory: Primes, Congruences, and Secrets - William Stein
• Elementary Real Analysis - Brian S. Thomson, Judith B. Bruckner, Andrew M. Bruckner
• Elements of Abstract and Linear Algebra - E. H. Connell
• Elements of Differential and Integral Calculus (1911) - William Anthony Granville (PDF)
• Essentials of Metaheuristics - Sean Luke
• First Course in Algebra (1910) - Herbert E. Hawkes, William A. Luby, Frank C. Touton (PDF)
• Foundations of Combinatorics with Applications - Edward A. Bender, S. Gill Williamson
• Foundations of Constructive Probability Theory - Yuen-Kwok Chan (PDF)
• Geometry with an Introduction to Cosmic Topology - Michael P. Hitchman
• Graph Theory
• How We Got from There to Here: A Story of Real Analysis - Robert Rogers, Eugene Boman
• Introduction to Modern Statistics - Mine Çetinkaya-Rundel, Johanna Hardin (HTML, PDF) (email address required for PDF)
• Introduction to Probability - Charles M. Grinstead, J. Laurie Snell (PDF)
• Introduction to Probability and Statistics Spring 2014
• Introduction to Proofs - Jim Hefferon
• Introduction to Real Analysis - William F. Trench
• Introduction to Statistical Thought - Michael Lavine
• Introductory Statistics for the Life and Biomedical Sciences - Julie Vu, David Harrington
• Kalman and Bayesian Filters in Python
• Knapsack Problems - Algorithms and Computer Implementations - Silvano Martello, Paolo Toth
• Lecture Notes of Linear Algebra - Dr. P. Shunmugaraj, IIT Kanpur (PDF)
• Lecture Notes on Linear Algebra - Dr. Arbind K Lal, Sukant Pati (PDF) (:construction: in process)
• Lies, Damned Lies, or Statistics: How to Tell the Truth with Statistics - Jonathan A. Poritz (PDF)
• Linear Algebra - David Cherney et al. (PDF)
• Linear Algebra - Jim Hefferon
• Linear Algebra Done Wrong - Sergei Treil
• Linear Algebra, Infinite Dimensions, and Maple - James Herod
• Linear Methods of Applied Mathematics - Evans M. Harrell II, James V. Herod
• Magic Squares and Cubes (1917) - W. S. Anderson (PDF)
• Math in Society - David Lippman
• Mathematical Analysis I - Elias Zakon
• Mathematical Discovery - Andrew M. Bruckner, Brian S. Thomson, Judith B. Bruckner
• Mathematical Logic - an Introduction (PDF)
• Mathematical Reasoning: Writing and Proof - Ted Sundstrom
• Mathematics, MTH101A - P. Shunmugaraj, IIT Kanpur
• Modern Statistics for Modern Biology - Susan Holmes, Wolfgang Huber
• Multivariable Calculus - George Cain, James Herod
• Non-Uniform Random Variate Generation - Luc Devroye (PDF)
• Notes on Diffy Qs - Jiří Lebl
• Number Theory - Holden Lee MIT
• Number Theory: In Context and Interactive - Karl-Dieter Crisman (HTML, PDF)
• Odds and Ends: Introducing Probability & Decision with a Visual Emphasis - Jonathan Weisberg
• Online Statistics Education - David Lane
• OpenIntro Statistics - David M. Diez, Christopher D. Barr, Mine Çetinkaya-Rundel
• ORCCA: Open Resources for Community College Algebra - Portland Community College
• Ordinary Differential Equations - Wikibooks
• Paul's Online Notes: Algebra, Calculus I-III and Differential Equations - Paul Dawkins @ Lamar University
• Plane Geometry (1913) - George Wentworth, David Eugene Smith (PDF)
• Planes and Spherical Trigonometry (1915) - George Wentworth, David Eugene Smith (PDF)
• Precalculus - Carl Stitz, Jeff Zeager (PDF)
• Probability and Statistics Cookbook
• Probability and Statistics EBook
• Probability: Lectures and Labs - Mark Huber
• Recreations in Math - H. E. Licks (PDF)
• Sage for Undergraduates - Gregory Bard
• Second Course in Algebra - Herbert E. Hawkes, William A. Luby, Frank C. Touton (PDF)
• Seven Sketches in Compositionality: An Invitation to Applied Category Theory - Brendan Fong, David I. Spivak (PDF)
• Statistical Thinking for the 21st Century - Russell A. Poldrack
• Statistics Done Wrong - Alex Reinhart
• SticiGui - Philip Stark
• Tea Time Numerical Analysis - Leon Q. Brin
• The Open Logic Text - Open Logic Project (PDF)
• Think Bayes: Bayesian Statistics Made Simple - Allen B. Downey
• Think Stats: Probability and Statistics for Programmers - Allen B. Downey (using Python)
• Vector Calculus - Michael Corral
• Yet Another Introductory Number Theory Textbook - Jonathan A. Poritz (PDF)