Más de 70 libros totalmente gratis de los más renombrados catedráticos y científicos del mundo que mejorarán tus conocimientos en Ciencia de Datos e Inteligencia Artificial.
Por Homer Díaz
Si estás interesado en aprender Data Science, Machine Learning y Deep Learning, pero no estás interesado en gastar dinero en libros, estás definitivamente en el lugar indicado.
Hay un gran número de excelentes libros totalmente gratis de los más renombrados catedráticos y científicos del mundo. Todos ellos están disponibles online o en formato pdf y debidamente autorizados por las casas editoras o por los propios autores.
Los números de “Customer Reviews” y las citas (“Cited by”) son los datos que registran actualmente Amazon y Google Scholar respectivamente.
AI
- How to Build Your Career in AI, 2022 – [FREE pdf]
Data Science
- Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide, 2023 – [FREE pdf]
- Harvard CS197: AI Research Experiences, 2023 – [Online book] [Web]
- Data Science: A First Introduction, 2022 – [Online book]
- Review: “This book offers a clear, thoughtful, and systematic treatment of the fundamentals of data science, with accompanying R code. As its name implies, it is truly an introduction, and is suitable for those who wish to self-teach R and data science, as well as to college instructors teaching a first course in data science. With a diverse set of topics […] this book is a one-stop shop that will be a valuable resource for years to come.” ―Daniela Witten, University of Washington.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2017 – [FREE online book]
- Customer Reviews: 4.7 out of 5 – 1,214 ratings
- Cited by 738
- Introduction to Data Science: Data Analysis and Prediction Algorithms with R, 2019 – [FREE online book] [FREE pdf]
- Customer Reviews: 4.7 out of 5 – 28 ratings
- Advanced R, 2019 – [FREE online book]
- Customer Reviews: 4.8 out of 5 – 123 ratings
- Cited by 361
- ggplot2, 3rd edition: Elegant Graphics for Data Analysis (Use R) – [FREE online book]
- Customer Reviews: 4.4 out of 5 – 134 ratings
- Cited by 39,838
- R Graphics Cookbook: Practical Recipes for Visualizing Data, 2018 – [Online book]
- Customer Reviews: 4.5 out of 5 – 71 ratings
- Data Visualization: A practical introduction, 2018 – [Online book]
- Customer Reviews: 4.7 out of 5 – 182 ratings
- R Programming for Data Science, 2020 – [Online book] [FREE pdf]
- Customer Reviews: 4.2 out of 5 – 19 ratings
- Exploratory Data Analysis with R, 2020 – [Online book] [FREE pdf]
- Customer Reviews: 4.3 out of 5 – 7 ratings
- Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, 2022 – [Online book]
- Customer Reviews: 4.5 out of 5 – 26 ratings
- Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, 2019 – [Online book]
- Customer Reviews: 4.6 out of 5 – 151 ratings
- Modern Statistics for Modern Biology, 2019 – [Online book]
- Customer Reviews: 4.9 out of 5 – 18 ratings
- Geocomputation with R: A book on geographic data analysis, visualization and modeling, 2020 – [Online book]
- Customer Reviews: 5 out of 5 – 8 ratings
- Data Science Live Book: An intuitive and practical approach to data analysis, data preparation and machine learning, suitable for all ages!, 2019 – [Online book]
- Customer Reviews: 4.2 out of 5 – 4 ratings
- Data Science: Theories, Models, Algorithms, and Analytics, 2017 – [Online book]
- Python Data Science Handbook: Essential Tools for Working with Data, 2016 – [FREE online book]
- Customer Reviews: 4.5 out of 5 – 513 ratings
- Cited by 486
- Data Science at the Command Line: Obtain, Scrub, Explore, and Model Data with Unix Power Tools, 2021 – [Free online book]
- Customer Reviews: 4.4 out of 5 – 15 ratings
- The Art of Data Science, 2017 [FREE online book]
- Customer Reviews: 4.6 out of 5 | 36 ratings
- The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists, 2015 – [FREE pdf]
- Customer Reviews: 4.5 out of 5 – 46 ratings
- Foundations of Data Science, 2020 – [FREE pdf]
- Customer Reviews: 4.7 out of 5 – 32 ratings
Artículo relacionado: Master of Science in Data Science: A 27 Créditos de Graduarme de CU Boulder
Time Series Forecast
- Forecasting: Principles and Practice, 2018 (2nd Edition) – [Online book]
- Customer Reviews: 4.6 out of 5 – 126 ratings
- Cited by 4,455
- Forecasting: Principles and Practice, 2021 (3rd Edition) – [Online book]
Machine Learning
- Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning, 2022 – [Free pdf]
- Explaining Predictions from Machine Learning Models: Algorithms, Users, and Pedagogy, 2022 – [FREE pdf]
- Pen and Paper: Exercises in Machine Learning, 2022 – [FREE pdf]
- Interpretable Machine Learning, 2022 – [Online book]
- Cited by 2,126
- Probabilistic Machine Learning: An Introduction, 2022 – [FREE pdf]
- Customer Reviews: 4.3 out of 5 – 21 ratings
- Probabilistic Machine Learning: Advanced Topics, 2023 – [FREE pdf]
- Algorithms for Convex Optimization, 2021 – [FREE pdf]
- Customer Reviews: 5 out of 5 – 2 ratings
- Statistics and Machine Learning in Python, 2021 [Online book]
- High-Dimensional Probability: An Introduction with Applications in Data Science, 2018 – [FREE pdf]
- Customer Reviews: 4.7 out of 5 – 51 ratings
- Cited by 1,634
- 🏅Winner, 2019 PROSE Award for Mathematics
- Mathematics for Machine Learning, 2020 – [FREE pdf]
- Customer Reviews: 4.7 out of 5 – 428 ratings
- Cited by 265
- Testimony: “This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.” Pieter Abbeel, University of California, Berkeley.
- The Matrix Cookbook – [FREE pdf]
- Cited by 2,709
- Machine Learning from Scratch: Derivations in Concept and Code, 2020 – [Online book]
- Understanding Machine Learning: From Theory to Algorithms, 2014 – [FREE pdf]
- Customer Reviews: 4.3 out of 5 – 172 ratings
- Cited by 4,532
- Testimony: “This elegant book covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data.” Bernhard Schölkopf, Max Planck Institute for Intelligent Systems
- Pattern Recognition and Machine Learning, 2006 – [FREE pdf]
- Customer Reviews: 4.6 out of 5 – 547 ratings
- Cited by 56,972
- Testimony: “This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas…A strong feature is the use of geometric illustration and intuition…This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group.” John Maindonald for the Journal of Statistical Software
- Foundations of Machine Learning, 2018 – [FREE pdf]
- Customer Reviews: 4.2 out of 5 – 25 ratings
- Cited by 3,988
- Approaching (Almost) Any Machine Learning Problem (AAAMLP), 2020 – [FREE pdf]
- Customer Reviews: 4.5 out of 5 – 516 ratings
- 🏅🏅🏅🏅Abhishek Thakur, the author of this book, is a data scientist and well known as the World’s First Quadruple Grandmaster on Kaggle. His passion lies in solving difficult world problems through data science. Abhishek did his Bachelors in Electronics Engineering from India and moved to Germany for pursuing MSc from University of Bonn, Germany with a focus on image processing and computer vision. He dropped out of PhD in 2015 and since then has been working in industries.
- Machine Learning Cheat Sheet: Classical equations, diagrams and tricks in machine learning, 2017 (Proto-book) – [FREE pdf]
- Machine Learning Yearning: Technical Strategies for AI Engineers, In the Era of Deep Learning, by Andrew Ng, 2018 – [FREE pdf]
- Cited by 162
- Machine Learning, by Tom Mitchell, McGraw Hill, 1997 [FREE pdf]
Artículo relacionado: Seminario: Machine Learning for the Working Mathematician (FREE)
Job Interviews
Prepárate para las entrevistas de trabajo en las áreas de Machine Learning, Data Science y Deep Learning
- Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI, 2021 – [FREE pdf]
- Machine Learning Bites: An interview guide on common Machine Learning concepts, best practices, definitions, and theory, 2022 [Online book]
- Introduction to Machine Learning Interviews Book, 2021 – [Online book]
- Fundamental Data Science | 800 Data Science Questions: Fundamental questions and answers in statistics, data analysis, machine learning, and deep learning, 2021 – [FREE pdf]
Probability & Statistics
- An Introduction to Statistical Learning with Applications in R, 2021 – [FREE pdf]
- Customer Reviews: 4.7 out of 5 – 1,482 ratings
- Cited by 13,490
- Introduction to Probability for Data Science, 2021 (MATLAB, Python, Julia, and R) – [FREE online book & FREE pdf]
- Customer Reviews: 4.8 out of 5 – 5 ratings
- Causal Inference: What If, 2020 [FREE pdf]
- Cited by 2,109
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2017 [FREE pdf]
- Customer Reviews: 4.6 out of 5 – 841 ratings
- Cited by 726
- Computer Age Statistical Inference: Algorithms, Evidence and Data Science, 2021 [FREE pdf]
- Customer Reviews: 4.7 out of 5 – 63 ratings
- Cited by 997
- 🏅Winner, 2017 PROSE Award for Computing and Information Sciences
- OpenIntro Statistics, 2022 – [FREE pdf]
- Customer Reviews: 4.4 out of 5 – 458 ratings
- Cited by 238
- Probability and Statistics Cookbook, 2021 – [FREE pdf]
- Statistical Learning with Sparsity: The Lasso and Generalizations, 2015 – [FREE pdf]
- Customer Reviews: 4.9 out of 5 – 22 ratings
- Cited by 2,651
- Introduction to Probability, 2019 – [FREE online book]
- Customer Reviews: 4.5 out of 5 – 80 ratings
- Cited by 209
- About the book: Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty.
Artículo relacionado: Introducción a Deep Learning en 170 Videos
Deep Learning
- Understanding Deep Learning, 2023 – [FREE draft pdf]
- The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks, 2022 [FREE pdf]
- Applications of Deep Neural Networks with Keras, 2022 [FREE pdf] [Code] [Website]
- The Mathematical Engineering of Deep Learning, 2022 [FREE pdf]
- Dive into Deep Learning: Interactive deep learning book with code, math, and discussions. Implemented with NumPy/MXNet, PyTorch, and TensorFlow, 2022 – [FREE online book & FREE pdf]
- Cited by 294
- Machine Learning with Neural Networks: An Introduction for Scientists and Engineers, 2021 [FREE pdf]
- Cited by 91
- Deep Learning, 2016 – [Online book]
- Customer Reviews: 4.4 out of 5 – 1,651 ratings
- Cited by 38,016
- Testimony: “Deep learning has taken the world of technology by storm since the beginning of the decade. There was a need for a textbook for students, practitioners, and instructors that includes basic concepts, practical aspects, and advanced research topics. This is the first comprehensive textbook on the subject, written by some of the most innovative and prolific researchers in the field. This will be a reference for years to come.” ―Yann LeCun, Director of AI Research, Facebook; Silver Professor of Computer Science, Data Science, and Neuroscience, New York University
- Neural Networks and Deep Learning: Introduction to the core principles, 2015 – [FREE online book]
- Cited by 3,637
- Deep Learning with Python, 2021 – [FREE online book]
- Customer Reviews: 4.5 out of 5 – 1,117 ratings
- Cited by 3,312
- Companion Jupyter notebooks for the book “Deep Learning with Python” [Notebooks]
- The Matrix Calculus You Need For Deep Learning, 2018 – [FREE online book] [FREE pdf]
- Cited by 18
- Physics-based Deep Learning, 2021 – [FREE online book] [FREE pdf]
Graph Neural Networks (Geometric Deep Learning)
- Graph Neural Networks Foundations, Frontiers, and Applications, 2022 (FREE pdf)
- Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, 2021 – [FREE pdf]
- Deep Learning on Graphs, 2021 – [FREE pdf]
- Customer Reviews: 5 out of 5 – 4 ratings
- Machine Learning on Graphs: A Model and Comprehensive Taxonomy, 2020 – [FREE pdf]
- Graph Representation Learning, 2020 – [FREE pdf]
- 4 out of 5 – 15 ratings
- Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview, 2019 – [FREE pdf]
Artículo relacionado: Graph Neural Networks: libros, cursos, datasets, librerías, charlas y más
Bayesian Statistics
- Bayesian Optimization, 2022 [FREE pdf]
- Bayesian Models of Perception and Actions: An Introduction, 2022 [FREE pdf]
- Think Bayes: Bayesian Statistics in Python, 2021 – [FREE online book]
- Customer Reviews: 4.3 out of 5 – 22 ratings
- Cited by 110
- Bayesian Reasoning and Machine Learning, 2020 – [FREE pdf]
- Customer Reviews: 4.1 out of 5 | 69 ratings
- Cited by 1,988
Gaussian Processes
- Gaussian Processes for Machine Learning, 2006 – [FREE pdf]
- Customer Reviews: 4.7 out of 5 – 48 ratings
- Cited by 1,812
- 🏅Winner: 2009 DeGroot Prize of the International Society for Bayesian Analysis
Reinforcement Learning
- Reinforcement Learning: An Introduction, 2018 – [FREE pdf]
- Customer Reviews: 4.6 out of 5 – 380 ratings
- Cited by 49,947
- Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control, 2022 – [FREE pdf]
- Algorithms for Decision Making, 2022 – [FREE pdf]
Artículo relacionado: Curso: Reinforcement Learning – Arizona State University
Data Mining
- Mining of Massive Datasets, 2020 [FREE pdf]
- Customer Reviews: 4.4 out of 5 – 19 ratings
- Cited by 2,117
- Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2020 – [FREE online book]
- Customer Reviews: 4.6 out of 5 – 14 ratings
Natural Language Processing
- ChatGPT for Startups, 2023 – [FREE pdf]
- Text Mining with R: A Tidy Approach, 2017 – [FREE online book]
- Customer Reviews: 4.5 out of 5 – 120 ratings
- Cited by 653
- Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning (Synthesis Lectures on Human Language Technologies), 2020 – Jose Camacho Collados – [FREE pdf]
- Cited by 26
- Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit [FREE online book]
- Customer Reviews: 4.4 out of 5 – 167 ratings
- Cited by 7,423
Computer Vision
- Computer Vision: Algorithms and Applications, 2022 – [FREE pdf]
- Customer Reviews: 4.3 out of 5 – 90 rating
- Cited by 7,082