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Введение в статистическое обучение с примерами на языке Python, Джеймс Г., Уиттен Д., Хасти Т., Тибширани Р., Тейлор Дж., 2024.
     
   В этой книге доступным языком описывается все разнообразие форм статистического обучения. Рассматриваются линейная регрессия, классификация, методы повторной выборки, отбор и регуляризация, полиномиальная регрессия, сплайны, локальная регрессия, обобщенные аддитивные модели, деревья решений, метод опорных векторов, кластеризация, а также нейронные сети, анализ выживаемости и множественная проверка гипотез. Теоретическая часть дополнена примерами из реальной практики и разборами решений на языке Python.
Издание предназначено не только для опытных специалистов в области статистики, но и для тех, кто желает попробовать применить продвинутые техники статистического обучения при анализе своих данных.

The Machine Learning Handbook, A Practical Guide, To Data-Driven Innovation, Vanita B.
     
Фрагмент из книги.
The future of machine learning is marked by exciting advancements and emerging trends that promise to revolutionize the field. Explainable AI (XAI) aims to make models more transparent and interpretable, fostering trust and accountability. Automated Machine Learning (AutoML) democratizes machine learning by automating complex tasks and making it accessible to nonexperts. Federated learning enables collaborative model training while preserving data privacy and security. Quantum machine learning leverages the power of quantum computing to solve complex problems more efficiently. By staying informed about these future trends, you can leverage cutting-edge technologies to drive innovation and create impactful solutions in the evolving landscape of machine learning.

The Little Learner, Чудесное машинное обучение, Фридман Д.П., Мендхекар А., 2024.
     
   В книге охвачены все концепции, необходимые для интуитивного понимания работы глубоких нейронных сетей, включая тензоры, расширенные операторы, алгоритмы градиентного спуска, искусственные нейроны, полносвязные, сверточные сети и остаточные сети, а также автоматическое дифференцирование. Читатель начнет с азов глубокого обучения и познакомится с полной реализацией полезного приложения: распознавателя зашумленных сигналов азбуки Морзе.
Разговорный стиль, постепенное движение от простого к сложному, забавные иллюстрации, формат вопросов и ответов делают обучение доступным и увлекательным.
Для читателей, знающих математику на уровне средней школы и имеющих некоторый опыт программирования.

The Comprehensive Guide to Machine Learning Algorithms and Techniques, Ahmed M.M., 2024.
     
   This Book provides a comprehensive overview of various machine learning algorithms and techniques, categorized by their primary functions, such as regression, classification, clustering, optimization, and NLP. Each algorithm has been explained in terms of its main concept, purpose, use cases, mathematical background, loss function, pros and cons, and visual representation. This knowledge is crucial for selecting the right algorithm for specific problems and understanding the underlying mechanisms that drive their performance.

Starting Data Analytics with Generative AI and Python, Guja A., Siwiak M., 2025.
     
   This book can be read on three levels, so essentially you have three different books in front of you. At the highest level, it’s a book about data analytics, and you’ll find a basic introduction to the discipline, tools, algorithms, and some more advanced concepts, which together should allow you to perform analytics on your own. It will give you a high-level overview of data analytics as an initial step into the field.

Machine Learning for Tabular Data, Ryan M., Massaron L., 2025.
     
   Machine Learning for Tabular Data dives into a critical area of machine learning: working with tabular data. From the spreadsheets you use every day to the databases that power businesses, tabular data is everywhere. It’s the hidden gem hiding in plain sight. This book goes beyond just theory. It equips you to leverage the power of tabular data by teaching you machine learning techniques specifically designed for it. You’ll learn how to make sense of your data, uncover patterns, and build real-world applications — all with the added benefit of clear and interpretable results.

Reinforcement Learning for Finance, A Python-Based Introductio, Hilpisch Y., 2025.
     
   This book is intended as a concise, Python-based introduction to the major ideas and elements of RL and DQL as applied to finance. It should be useful to both students and academics as well as to practitioners in search of alternatives to existing financial theories and algorithms. The book expects basic knowledge of the Python programming language, object-oriented programming, and the major Python packages used in data science and machine learning, such as NumPy, pandas, matplotlib, scikit-learn, and TensorFlow.

RAG with Python Cookbook ER, Polzer D., 2026.
     
Фрагмент из книги.
In this book, you will often see functions from RAG frameworks like LangChain and LlamaIndex. This makes sense because these RAG frameworks are handy and offer many functions we need to create RAG applications. Nevertheless, check first if you really need them. They are still at an early stage and constantly changing, which can be challenging when deploying apps to production. Since they are merely a collection of more established frameworks, you could also use the standalone frameworks behind LangChain and LlamaIndex.