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Applied Supervised Learning with Python

You're reading from   Applied Supervised Learning with Python Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

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Product type Paperback
Published in Apr 2019
Publisher
ISBN-13 9781789954920
Length 404 pages
Edition 1st Edition
Languages
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Authors (2):
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Ishita Mathur Ishita Mathur
Author Profile Icon Ishita Mathur
Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Toc

Chapter 1. Python Machine Learning Toolkit

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Explain supervised machine learning and describe common examples of machine learning problems

  • Install and load Python libraries into your development environment for use in analysis and machine learning problems

  • Access and interpret the documentation of a subset of Python libraries, including the powerful pandas library

  • Create an IPython Jupyter notebook and use executable code cells and markdown cells to create a dynamic report

  • Load an external data source using pandas and use a variety of methods to search, filter, and compute descriptive statistics of the data

  • Clean a data source of mediocre quality and gauge the potential impact of various issues within the data source

Note

This chapter introduces supervised learning, Jupyter notebooks, and some of the most common pandas data methods.

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