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Hands-On Data Science and Python Machine Learning

You're reading from   Hands-On Data Science and Python Machine Learning Perform data mining and machine learning efficiently using Python and Spark

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787280748
Length 420 pages
Edition 1st Edition
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Statistics and Probability Refresher, and Python Practice 3. Matplotlib and Advanced Probability Concepts 4. Predictive Models 5. Machine Learning with Python 6. Recommender Systems 7. More Data Mining and Machine Learning Techniques 8. Dealing with Real-World Data 9. Apache Spark - Machine Learning on Big Data 10. Testing and Experimental Design

Summary

In this chapter, we talked about plotting and graphing your data and how to make your graphs look pretty using the matplotlib library in Python. We also walked through the concepts of covariance and correlation. We looked at some examples and figured out covariance and correlation using Python. We analyzed the concept of conditional probability and saw some examples to understand it better. Finally, we saw Bayes' theorem and its importance, especially in the medical field.

In the next chapter, we'll talk about predictive models.

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