Master Machine Learning. Master Scikit-learn algorithms and PyTorch deep learning architectures (English Edition) - Valencia Munoz Luis

Machine learning is transforming industries from healthcare to finance, and Python has become the lingua franca for building intelligent systems. PyTorch and Scikit-learn are two of the most powerful frameworks driving today's AI revolution, enabling developers to build everything from simple predictive models to sophisticated deep learning architectures.

This book takes you on a comprehensive journey from Python fundamentals through advanced deep learning. You will master essential libraries like NumPy, Pandas, and Matplotlib, and build classical ML models with Scikit-learn before exploring neural networks with PyTorch. Through 20 hands-on chapters, you will explore CNNs, RNNs, GANs, reinforcement learning, transformers, recommendation systems, NLP, time series analysis, and finally deploy models to Azure ML as production-ready APIs.

By the end of this book, you will have the hands-on expertise to build, train, and deploy advanced AI systems. Whether you are starting your ML journey or deepening your skills, you will gain the confidence to tackle real-world challenges and contribute meaningfully to the field of artificial intelligence.

WHAT YOU WILL LEARN

● Set up professional ML environments locally and in the cloud.

● Build and evaluate ML models using Scikit-learn algorithms.

● Design neural networks from scratch using the PyTorch framework.

● Implement CNNs, RNNs, GANs, and reinforcement learning systems.

● Apply NLP and computer vision techniques to real-world problems.

● Build recommendation systems and time series forecasting models.

● Deploy trained models to Azure ML as production REST APIs.

WHO THIS BOOK IS FOR

This book is for Python developers, data scientists, and engineers aiming to master AI. Beginners and professionals should possess basic Python knowledge before exploring Scikit-learn and PyTorch to build, optimize, and deploy production-ready machine learning models across diverse industrial applications.