This is the third edition of a guide to machine learning and deep learning with Python. Authors Sebastian Raschka and Vahid Mirjalili aim to teach the principles behind machine learning, so developers can build models and applications for themselves.Updated for TensorFlow 2.0, this new edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover 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.

Authors: Sebastian Raschka and Vahid Mirjalili
Publisher: Packt Publishing
Date: December 2019
Pages: 770
ISBN: 978-1789955750
Print: 1789955750
Kindle: B07VBLX2W7
Audience: Python developers interested in machine learning
Level: Intermediate/advanced
Category: Artificial Intelligence

• 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

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