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Python Machine Learning: Machine Learning and Deep Learning with Python,scikit-learn,and TensorFlow,2nd Edition
by 作者: Sebastian Raschka - Vahid Mirjalili
ISBN-10 书号: 1787125939
ISBN-13 书号: 9781787125933
Edition 版本: 2nd
Publisher Finelybook 出版日期: 2017-09-20
Pages: 622
Publisher Finelybook 出版社: Packt
Book Description
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. Thoroughly updated using the latest Python 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 deep learning library. The scikit-learn code has also been fully updated to include recent 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 in today’s world.
If you’ve read the first edition of this book,you’ll be delighted to find a new 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 more deeply than ever before,and get essential coverage of the Keras neural network library,along with the most recent updates to scikit-learn.
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
Master machine learning techniques using challenging real-world data
Master deep neural network implementation using the TensorFlow library
Ask new questions of your data through machine learning models and neural networks
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
