Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition - Softcover

Raschka; Sebastian; Mirjalili; Vahid

Softcover

ISBN 10: 1789955750 ISBN 13: 9781789955750

Verlag: Packt Publishing, 2019

Inhaltsangabe

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

Key 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

Book Description

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.

What you will learn

• 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

Who This Book Is For

If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

Table of Contents

1. Giving Computers the Ability to Learn from Data
2. Training Simple ML Algorithms for Classification
3. ML Classifiers Using scikit-learn
4. Building Good Training Datasets - Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Ensemble Learning
8. Applying ML to Sentiment Analysis
9. Embedding a ML Model into a Web Application
10. Predicting Continuous Target Variables with Regression Analysis
11. Working with Unlabeled Data - Clustering Analysis
12. Implementing Multilayer Artificial Neural Networks
13. Parallelizing Neural Network Training with TensorFlow
14. TensorFlow Mechanics
15. Classifying Images with Deep Convolutional Neural Networks
16. Modeling Sequential Data Using Recurrent Neural Networks
17. GANs for Synthesizing New Data
18. RL for Decision Making in Complex Environments

Über diesen Titel

Über die Autorin bzw. den Autor

Sebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python.While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background.His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017, as well as the ACM Computing Reviews' Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle.Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University.Vahid picked Python as his number-one choice of programming language, and throughout his academic and research career he has gained tremendous experience with coding in Python. He taught Python programming to the engineering class at Michigan State University, which gave him a chance to help students understand different data structures and develop efficient code in Python.While Vahid's broad research interests focus on deep learning and computer vision applications, he is especially interested in leveraging deep learning techniques to extend privacy in biometric data such as face images so that information is not revealed beyond what users intend to reveal. Furthermore, he also collaborates with a team of engineers working on self-driving cars, where he designs neural network models for the fusion of multispectral images for pedestrian detection.

Details zur Ausgabe

• Verlag: Packt Publishing
• Erscheinungsdatum: 2019
• Sprache: Englisch
• ISBN 10: 1789955750
• ISBN 13: 9781789955750
• Einband: Taschenbuch
• Auflage: 3
• Anzahl der Seiten: 770
• Kontakt zum Hersteller: Packt; https://www.packtpub.com/; ; 2nd Floor, 35 Livery Street; Birmingham; B32PB; Vereinigtes Königreich
• Verantwortliche Person: gpsr@libri.de; gpsr@libri.de; ; Friedensallee 273; Hamburg; 22763; Deutschland