Free Programming Books

📘 Microservices Best Practices for Java ✍️ Authors: Michael Hofmann, Erin Schnabel, Katherine Stanley 🗓 Year: 2016 📄 Pages: ~134 🧠 This book covers Microservices best practices for Java. It focuses on creating cloud native applications using the latest version of IBM WebSphere Application Server Liberty, IBM Bluemix and other Open Source Frameworks in the Microservices ecosystem to highlight Microservices best practices for Java.

📘 Foundations of Robotics ✍️ Authors: Damith Herath, David St-Onge 🗓 Year: 2022 📄 Pages: ~543 🧠 This open access book introduces key concepts in robotics in an easy to understand language using an engaging project-based approach. It covers contemporary topics in robotics, providing an accessible entry point to fundamentals in all the major domains. The book also introduces the reader to the Robot Operating System (ROS), the ubiquitous software and algorithmic framework used by researchers and the industry.

📘 Beyond Coding ✍️ Author: David Tuffley 🗓 Year: 2025 📄 Pages: ~185 🧠 In today's competitive IT industry, technical skills alone are no longer enough to succeed. This book teaches essential soft skills that transform technical professionals into complete IT leaders. You will master clear communication for diverse audiences, build strong teams through collaboration and conflict resolution, and solve complex problems with structured thinking. The guide covers time management techniques, project management fundamentals, and stakeholder delivery methods. Additional modules address professional ethics, customer relations, cultural awareness, leadership influence, and personal growth.

📘 Introducing MLOps ✍️ Authors: Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann 🗓 Year: 2020 📄 Pages: ~180 🧠 More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time.

📘 Time Series Databases: New Ways to Store and Access Data ✍️ Authors: Ted Dunning, Ellen Friedman 🗓 Year: 2015 📄 Pages: ~82 🧠 Time series databases enable a fundamental step in the central storage and analysis of many types of machine data. This book shows you how to build scalable, high-performance time series databases using open source software on top of Apache HBase or MapR-DB. It focus on how to collect, store, and access large-scale time series data rather than the methods for analysis.

📘 The Joy of Cryptography ✍️ Authors: Mike Rosulek 🗓 Year: 2021 📄 Pages: ~286 🧠 This book is not a handbook that specifies which cryptographic algorithm to use in every situation, nor is it a guide for securely implementing production-ready cryptographic libraries. Instead, the book is intended for anyone who may need to secure information using cryptography and who is curious about the principles that make some systems secure and others insecure.

📘 The Svelte Handbook ✍️ Author: Flavio Copes 🗓 Year: 2019 📄 Pages: ~40 🧠 Svelte is an exciting Web framework that offers a fresh new take on how to build Web applications. If you are already experienced in React, Vue, Angular or other frontend frameworks you might be pleasantly surprised by Svelte.

📘 OOP - Learn Object Oriented Thinking and Programming ✍️ Author: Rudolf Pecinovský 🗓 Year: 2013 📄 Pages: ~527 🧠 The book draws on the author's extensive experience teaching children, university students, and professional programmers. He employs the Architecture First methodology, a approach developed in response to industry complaints that graduates, while often proficient at coding, typically lack skills in software architecture.

📘 Algorithmic Aspect of Machine Learning ✍️ Author: Ankur Moitra 🗓 Year: 2018 📄 Pages: ~249 🧠 This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy.

📘 Anyone Can Code: Algorithmic Thinking ✍️ Author: Michal P. Garstka 🗓 Year: 2023 📄 Pages: ~180 🧠 A modern, beginner-friendly book that teaches how to think algorithmically, with clear explanations, real code examples, and visual intuition. Perfect for building strong programming foundations.

📘 Dive into Deep Learning ✍️ Authors: Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola 📄 Pages: ~574 🧠 A modern, open-source deep learning book with code, maths and runnable examples, covering everything from basics to advanced models and practical tasks like vision and NLP. Great for hands-on learning with visual intuition and real code #deeplearning ──────────────────── 📘 One book at a time Building smarter developers 👉 @free_programming_books_bds 👈