scikit-learn Cookbook

Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation

scikit-learn Cookbook

Trent Hauck

3 customer reviews
Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation
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Book Details

ISBN 139781783989485
Paperback214 pages

Book Description

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across.

The book starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets.

Table of Contents

What You Will Learn

  • Address algorithms of various levels of complexity and learn how to analyze data at the same time
  • Handle common data problems such as feature extraction and missing data
  • Understand how to evaluate your models against themselves and any other model
  • Discover just enough math needed to learn how to think about the connections between various algorithms
  • Customize the machine learning algorithm to fit your problem, and learn how to modify it when the situation calls for it
  • Incorporate other packages from the Python ecosystem to munge and visualize your dataset

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Table of Contents

Book Details

ISBN 139781783989485
Paperback214 pages
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