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Free Data Science, Analytics, Machine Learning and IoT Books


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Probabilistic Models in the Study of Language. Probabilistic models in scientific work on language ranging from experimental data analysis to corpus work to cognitive modeling
Elementary Applied Topology.
Ordinary Differential Equations.
Machine Learning Logistics: Model Management in the Real World.
The Data Scientist's Guide to Apache Spark.
BI & Analytics on a Data Lake.
Ambient Intelligence. In this book a selection of unsolved problems which are considered key for ambient intelligence to become a reality, is analyzed and studied in depth
Affective Computing. Overview of state of the art research in Affective Computing (artificial emotional intelligence, or emotion AI)
Planning Algorithms. Planning algorithms for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory
The Quest for Artificial Intelligence: A history of ideas and achievement. The history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers.
Bio-Inspired Computational Algorithms and Their Applications. This book integrates contrasting techniques of genetic algorithms, artificial immune systems, particle swarm optimisation, and hybrid models to solve many real-world problems
A Quick and Gentle Guide to Constraint Logic Programming. Introductory to Constraint Logic Programming for solving combinatorial and continuous constraint satisfaction problems and constraint optimisation problems
Recent Advanced in Face Recognition.
Essentials of Metaheuristics. Covers a wide range of algorithms, representations, selection and modification operators, and related topics
Practical Artificial Intelligence Programming in Java. Written for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical AI programming techniques
Artificial Intelligence: Foundations of computational agents. Presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents
Logic for Computer Science: Foudations of automatic theorem proving. Covers the mathematical logic necessary to computer science, emphasising algorithmic methods for solving proofs
Simply Logical: Intelligent reasoning by example. An introduction to Prolog programming for artificial intelligence covering both basic and advanced AI material
Interpretable Machine Learning: A guide for making black box models explainable.
Algorithm Design: Parallel and Sequential.
A Year in Computer Vision.
Artificial Intelligence Now. In six distinct parts, this book covers the AI landscape and technology, homebuilt autonomous systems, natural language, use cases and integrating human and machine intelligence
A Gentle Introduction to Apache Spark. Learn why Spark is a popular choice for data analytics, what tools and features are available, its basic architecture and how to get started right away through interactive sample code
Buyer's Guide to Choosing the Right Data Science Platform.
The Ray Kurzweil Reader: A collection of essays by Ray Kurzweil published on KurzweilAI.net 2001-2003.
Synthetic Super Intelligence and the Transmutation of Humankind. A roadmap to the singularity and beyond
Breaking Data Science Open. Deliver collaboration, self-service and production deployment with open data science
Spark: The Definitive Guide. A deep dive into how Spark runs on a cluster; detailed examples in SQL, Python and Scala; Structured Streaming and Machine Learning; examples of GraphFrames and Deep Learning with TensorFrames
Artificial Intelligence. Documentation artificial intelligence and machine learning topics
Artificial Intelligence: A Modern Approach.
Computer Vision: Algorithms and Applications.
Serving Machine Learning Models. A guide to architecture, stream processing engines, and frameworks
Data Mining Algorithms in R.
Introduction to Machine Learning.
D3 Tips and Tricks. Over 600 pages of tips and tricks for using d3.js, one of the leading data visualization tools for the web.
Machine Learning, Neural and Statistical Classification.
Social Media Mining.
Advanced R. Designed primarily for R users who want to improve their programming skills and understanding of the language
Learn Python the Hard Way.
The Data Analytics Handbook. Interviews with data scientists, data analysts, CEOs, managers, and researchers at the cutting edge of the data science industry
Think Python: How to Think Like a Computer Scientist.
KB - Neural Data Mining with Python sources. Algorithms to extract the knowledge hidden inside data using Python language
SQL Tutorial. A quick start to SQL. It covers most of the topics required for a basic understanding of SQL and to get a feel of how it works
A First Course in Design and Analysis of Experiments. This text covers the basic topics in experimental design and analysis
Data Mining with Rattle and R. The art of excavating data for knowledge discovery
The Elements of Data Analytic Style. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks
Machine Learning - The Complete Guide.
R Programming. A practical guide to the R programming language
Automate the Boring Stuff with Python. Practical Python programming for total beginners
Think Stats: Exploratory Data Analysis in Python. An introduction to the practical tools of exploratory data analysis

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