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Introduction to Machine Learning with Python: A Guide for Data Scientists - Softcover
Book 6 of 7: Learning Python
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.
You’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, you’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
"synopsis" may belong to another edition of this title.
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.
"About this title" may belong to another edition of this title.
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Introduction to Machine Learning with Python: A Guide for Data Scientists
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O'Reilly Media (edition 1), 2016
ISBN 10: 1449369413
ISBN 13: 9781449369415
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Softcover. Condition: VERY GOOD. Very Good. Unmarked. Clean, unmarked interior. Softcover, clean & bright, some edge corner and shelf wear. No rips, chips, stains or tears. Binding solid. 2017 Edition (4th release 2018). Ships from USA, quickly and with care. _________________________________________________________________________ ____________________________________________________ SYNOPSIS & HISTORY: Introduction to Machine Learning with Python by Andreas C. Muller and Sarah Guido is a practical and accessible guide to modern machine learning techniques using Python and the scikit-learn library. Designed for students, programmers, analysts, and aspiring data scientists, the book emphasizes hands-on applications and real-world workflows rather than purely theoretical concepts. Covering classification, regression, clustering, model evaluation, parameter tuning, text processing, and data representation, the book demonstrates how machine learning can be applied to solve practical data problems efficiently. Featuring code examples, visual explanations, and project-based learning, this widely respected resource serves as an excellent introduction to applied machine learning and data science with Python. Seller Inventory # -50VG031625m1