David Paper
Logan, UT, USA
Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/9781484253724 . For more detailed information, please visit http://www.apress.com/source-code .
ISBN 978-1-4842-5372-4 e-ISBN 978-1-4842-5373-1
https://doi.org/10.1007/978-1-4842-5373-1
David Paper 2020
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Introduction
We apply the popular Scikit-Learn library to demonstrate machine learning exercises with Python code to help readers solve machine learning problems. The book is designed for those with intermediate programming skills and some experience with machine learning algorithms. We focus on application of the algorithms rather than theory. So, readers should read about the theory online or from other sources if appropriate. The reader should also be willing to spend a lot of time working through the code examples because they are pretty deep. But, the effort will pay off because the examples are intended to help the reader tackle complex problems.
The book is organized into eight chapters. Chapterputs all knowledge together to review and present findings in a holistic manner.
Download this books example data by clicking the Download source code button found on the books catalog page at https://www.apress.com/us/book/9781484253724 .
1. Introduction to Scikit-Learn
Scikit-Learn is a Python library that provides simple and efficient tools for implementing supervised and unsupervised machine learning algorithms. The library is accessible to everyone because it is open source and commercially usable. It is built on NumPY, SciPy, and matplolib libraries, which means it is reliable, robust, and core to the Python language.
Scikit-Learn is focused on data modeling rather than data loading, cleansing, munging or manipulating. It is also very easy to use and relatively clean of programming bugs.