программирование

Машинное обучение для абсолютных новичков, Вводный курс, изложенный простым языком, Теобальд О., 2024.
     
   «Машинное обучение для абсолютных новичков» Оливера Теобальда — это идеальная книга для тех, кто хочет изучить основы машинного обучения (ML) без опыта программирования. Книга содержит основные алгоритмы ML, наглядные примеры, практические работы и обучение классической статистике. Руководство включает в себя материалы по загрузке бесплатных наборов данных, методы очистки и подготовки данных для анализа, основы работы нейронных сетей и многое другое.

Large Language Models Projects, Martra P., 2024.
     
   In this book, I have attempted to provide an explanation that guides the reader from merely using large language models via API to defining large solutions where these models play a significant role. To achieve this, various techniques are explained, including prompt engineering, model training and evaluation, and the use of tools such as vector databases. The importance of these large language models is not only discussed, but great emphasis is also placed on the handling of embeddings, which is essentially the language understood by large language models.

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.