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Machine Learning System Design, Babushkin V., Kravchenko A., 2025.
     
   As ML and AI are getting bigger and bigger these days, there are many books and courses on algorithms, domains, and other specific aspects. However, they don’t provide an entire vision. This leads to the problem Arseny and Valerii have seen in multiple companies, where solid engineers successfully build scattered subcomponents that can’t be combined into a fully functioning, reliable system. This book aims to, among other things, fill this gap.
This book is not beginner friendly. We expect our readers to be familiar with ML basics (you can understand an ML textbook for undergraduate students) and to be fluent in applied programming (you have faced real programming challenges outside the studying sandbox).

Machine Learning in 30 Days, The Complete Beginner’s Guide, Jain A., 2025.
     
   Mastering machine learning requires a structured approach to ensure consistent progress and deep comprehension of concepts. This book provides a 30-day roadmap, guiding you from the basics to advanced ML techniques with step-by-step explanations, practical examples, and realworld applications.

Machine Learning Algorithms in Depth, Smolyakov V., 2024.
     
   This book dives into the design of ML algorithms from scratch. Throughout the book, you will develop mathematical intuition for classic and modern ML algorithms and learn the fundamentals of Bayesian inference and deep learning as well as data structures and algorithmic paradigms in ML.
Understanding ML algorithms from scratch will help you choose the right algorithm for the task, explain the results, troubleshoot advanced problems, extend algorithms to new applications, and improve the performance of existing algorithms.

LLMs in Production, Brousseau C., Sharp M., 2025.
     
   LLMs in Production is not your typical Data Science book. In fact, you won’t find many books like this at all in the data space mainly because creating a successful data product often requires a large team—data scientists to build models, data engineers to build pipelines, MLOps engineers to build platforms, software engineers to build applications, product managers to go to endless meetings, and, of course, for each of these, managers to take the credit for it all despite their only contribution being to ask questions, oftentimes the same questions repeated, just trying to understand what’s going on.

Learn OpenCV with Python by Exercise, Stroup J.L.
     
   OpenCV is one of the most popular computer vision libraries. If you want to start your journey in the field of computer vision, then a thorough understanding of the concepts of OpenCV is of paramount importance.
In this article, I will try to introduce the most basic and important concepts of OpenCV in an intuitive manner.

Learn Go with Pocket-Sized Projects, Latour A., Chaiehloudj D., Bertrand P.
     
   This book is for developers who want to learn the language in a fun and interactive way, and be comfortable enough to use it professionally. Each chapter is an independent pocket-sized project. The book covers the specificities of the language, such as implicit interfaces and how they help in test design. Testing the code is included throughout the book. We want to help the reader become a good modern software developer while using the Go language.
This book also contains tutorials for command-line interfaces, and for both REST and gRPC microservices, showing how the language is great for cloud computing. It finishes with a project that uses TinyGo, the compiler for embedded systems.

LangChain in Action, MEAP, Infante R.
     
   "LangChain in Action" is structured to cater both to beginners and seasoned professionals. It offers a comprehensive look into the foundational technologies and advanced techniques in the realm of LLMs. Whether you are new to programming or an experienced developer, you will find the content approachable yet enriching, especially with practical code examples to enhance your engagement.
The insights in this book are drawn from my real-world experiences and continuous learning in a field that evolves almost daily. I cover a range of topics from running open source LLMs locally to advanced RAG techniques and fine-tuning methods. My goal is to provide a resource that I wish had been available to me—an accessible, practical guide that empowers you to navigate and excel in this emerging domain.

GPT-4, Руководство по использованию API Open AI, Аймен Эль Амри, 2024.
     
   В книге рассказывается о том, как использовать генеративные текстовые модели поколений GPT-3.5 и GPT-4 для создания приложений различного назначения, в числе которых интерактивный психотерапевт, интеллектуальный голосовой помощник, система рекомендации товаров, генератор заметок в соцсетях, система распознавания речи и многие другие. Вы научитесь использовать векторные базы данных, узнаете, как управлять уровнем креативности моделей GPT, применять современные методы генерирования высококачественного текста, и даже организуете диалог между двумя чат-ботами. Примеры и практические упражнения помогут закрепить пройденный материал.
Издание предназначено для тех, кто владеет основами языка программирования Python и собирается использовать GPT в реальных сценариях для решения прикладных задач.