Machine Learning Engineer Mastery Pack

Deepen your theoretical knowledge and enhance your ability to solve complex machine learning problems with structured guidance. Gain the confidence to engage with advanced ML literature and tailor algorithms to meet your project requirements.

Fully updated with PyTorch and the latest additions to scikit-learn. Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: transformers and graph neural networks.

Develop your Python machine learning skills with the latest techniques. Dive into NLP transformers, multimodal vision models, and best practices. Explore neural networks, clustering, and GPT with hands-on examples in PyTorch and TensorFlow.

Predict the future with confidence with this practical guide on building and deploying powerful time series forecasting models. New content on transformers and probabilistic modeling makes it a must-read for tackling complex forecasting challenges.

This hands-on book will help you make your machine learning models fairer, safer, and more reliable and in turn improve business outcomes. Every chapter introduces a new mission where you learn how to apply interpretation methods to realistic use cases with methods that work for any model type as well as methods specific for deep neural networks.

This expanded third edition of the popular Deep Reinforcement Learning Hands-On teaches cutting-edge techniques through new projects and over 20 practical chapters, fully updated for PyTorch 2.3, Gymnasium, stable-baselines3, and others.

As machine learning algorithms become more powerful, data scientists need a clear grasp of their key components. This book explains the core math principles underpinning the most used algorithms, detailing their importance and practical applications.

Bayesian inference uses probability distributions and Bayes' theorem to build flexible models. This book uses PyMC to abstract all mathematical and computational details from this process, allowing readers to solve a range of data science problems.

Python Feature Engineering Cookbook, Third Edition, walks you through tools and methods to craft powerful features from tabular, transactional, and time-series data for robust machine learning models.

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models.

With the expert help of Brett Lantz, you'll learn how to uncover key insights and make new predictions using this hands-on, practical guide to machine learning with R. This 10th Anniversary Edition features an overview of R and plenty of new use cases for advanced users. The book is fully updated to R 4.0.0, with newer and better examples and the most up-to-date R libraries, advice on ethical and bias issues, and new chapters that dive deeper into advanced modeling techniques and methods for using big data in R.

This latest edition of the bestselling Kaggle Book offers a clear path to cracking Kaggle competitions and serves as an all-encompassing guide to upgrading your skills and career as a data scientist.

Scikit-learn is a powerful, open-source ML library for Python that provides efficient tools for model development and deployment. It's a user-friendly framework for implementing ML algorithms and is used widely by industry professionals.

Causal Inference and Discovery in Python is a comprehensive exploration of the theory and techniques at the intersection of modern causality and machine learning. It covers fundamental concepts of Pearlian causal inference, explains the theory, and provides step-by-step code examples for both traditional and advanced causal inference and discovery techniques.

This book will help you explore the implementation of various well-known machine learning algorithms work with various C++ frameworks and libraries. You'll learn basic-to-advanced machine learning concepts with practical and easy-to-follow examples.

This latest edition emphasizes AI risk management, the AI/ML adoption journey, and the emerging field of generative AI, highlighting the increasing importance of developing secure and scalable ML platforms across industries.

This book teaches you how to recognize problems in cybersecurity where AI adds value, design and implement efficient solutions, and understand where and when to apply these methods to solve cybersecurity problems.

This third edition is updated with TensorFlow 2 and the latest additions to scikit-learn. Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: reinforcement learning and generative adversarial networks.