Python Machine Learning, Second Edition

Raschka, Sebastian|Mirjalili, Vahid

ISBN 10: 1787125939 / ISBN 13: 9781787125933

Verlag: Packt Publishing, 2017

Beschreibung

&Uumlber den AutorSebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving.

Bestandsnummer des Verkäufers 448326331

Bibliografische Details

• Titel: Python Machine Learning, Second Edition
• Verlag: Packt Publishing
• Erscheinungsjahr: 2017
• Sprache: Englisch
• ISBN-10: 1787125939
• ISBN-13: 9781787125933
• Einband: Softcover
• Auflage: 2. Auflage
• Zustand: New

Über diesen Titel

Inhaltsangabe

Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.

Key Features

• Second edition of the bestselling book on Machine Learning
• A practical approach to key frameworks in data science, machine learning, and deep learning
• Use the most powerful Python libraries to implement machine learning and deep learning
• Get to know the best practices to improve and optimize your machine learning systems and algorithms

Book Description
.
Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.

Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.

Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities.

If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.

What You Will Learn

• Understand the key frameworks in data science, machine learning, and deep learning
• Harness the power of the latest Python open source libraries in machine learning
• Explore machine learning techniques using challenging real-world data
• Master deep neural network implementation using the TensorFlow 1.x library
• Learn the mechanics of classification algorithms to implement the best tool for the job
• Predict continuous target outcomes using regression analysis
• Uncover hidden patterns and structures in data with clustering
• Delve 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 and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.

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.