Introduction to Machine Learning with Python: A Guide for Data Scientists

Sarah Guido

ISBN 10: 1449369413 / ISBN 13: 9781449369415

Published by O Reilly Media, Inc, USA, 2016

About this Item

Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject. Num Pages: 400 pages. BIC Classification: UMW. Category: (XV) Technical / Manuals. Dimension: 233 x 178. . . 2016. 1st Edition. Paperback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9781449369415

Bibliographic Details

• Title: Introduction to Machine Learning with Python: A Guide for Data Scientists
• Publisher: O Reilly Media, Inc, USA
• Publication Date: 2016
• Language: English
• ISBN 10: 1449369413
• ISBN 13: 9781449369415
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

Youâ??ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, youâ??ll learn:

• Fundamental concepts and applications of machine learning
• Advantages and shortcomings of widely used machine learning algorithms
• How to represent data processed by machine learning, including which data aspects to focus on
• Advanced methods for model evaluation and parameter tuning
• The concept of pipelines for chaining models and encapsulating your workflow
• Methods for working with text data, including text-specific processing techniques
• Suggestions for improving your machine learning and data science skills

About the Authors

Andreas Müller received his PhD in machine learning from the University of Bonn. After working as a machine learning researcher on computer vision applications at Amazon for a year, he recently joined the Center for Data Science at the New York University. In the last four years, he has been maintainer and one of the core contributor of scikit-learn, a machine learning toolkit widely used in industry and academia, and author and contributor to several other widely used machine learning packages. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

Sarah is a data scientist who has spent a lot of time working in start-ups. She loves Python, machine learning, large quantities of data, and the tech world. She is an accomplished conference speaker, currently resides in New York City, and attended the University of Michigan for grad school.