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Introduction to Machine Learning with Python: A Guide for Data Scientists Spiral-Bound |

Andreas C. Müller, Sarah Guido

★★★★☆+ from 101 to 500 ratings

$46.17 - Free Shipping

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
Publisher: Ingram Publisher Services
Original Binding: Trade Paperback
Pages: 398 pages
ISBN-10: 1449369413
Item Weight: 1.94 lbs
Dimensions: 7.0 x 1.49 x 9.2 inches
Customer Reviews: 4 out of 5 stars 101 to 500 ratings
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.