Librería Portfolio

Master the frameworks, models, and techniques that enable machines to ´learn´ from data
Use scikit-learn for machine learning and TensorFlow for deep learning
Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more
Build and train neural networks, GANs, and other models
Discover best practices for evaluating and tuning models
Predict continuous target outcomes using regression analysis
Dig deeper into textual and social media data using sentiment analysis
About
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you´ll keep coming back to as you build your machine learning systems.

Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.

Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It´s also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.

This book is your companion to machine learning with Python, whether you´re a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

Features
Third edition of the bestselling, widely acclaimed Python machine learning book
Clear and intuitive explanations take you deep into the theory and practice of Python machine learning
Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices.