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Mastering Excel with ChatGPT, A Guide to Automation and Efficiency, Tang Y.
     
   Welcome to your journey of mastering Excel! Before we dive into the complexities and advanced functions of this tool, it is crucial to understand its interface. It helps you become familiar with its components and navigation.

Машинное обучение, Основы, Николенко С., 2025.
     
   Машинное обучение давно уже стало синонимом искусственного интеллекта. Оно проникло во многие аспекты нашей жизни и стало одной из важнейших областей современной науки. Эта книга — путеводитель по ключевым идеям машинного обучения. Вы узнаете, как методы машинного обучения получаются из основных принципов теории вероятностей, пройдёте путь от теоремы Байеса до обобщенных линейных моделей и узнаете в лицо тех китов, на которых стоит весь современный искусственный интеллект. Множество увлекательных кейсов, практических примеров и интересных задач — от анализа ретроспективных научных исследований до эффекта «горячей руки» в баскетболе — помогут разобраться в самых сложных концепциях. Кроме того, книга может лечь в основу базовых курсов по машинному обучению.

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.