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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

Statistics and Probability Refresher, and Python Practice

In this chapter, we are going to go through a few concepts of statistics and probability, which might be a refresher for some of you. These concepts are important to go through if you want to be a data scientist. We will see examples to understand these concepts better. We will also look at how to implement those examples using actual Python code.

We'll be covering the following topics in this chapter:

  • Types of data you may encounter and how to treat them accordingly
  • Statistical concepts of mean, median, mode, standard deviation, and variance
  • Probability density functions and probability mass functions
  • Types of data distributions and how to plot them
  • Understanding percentiles and moments
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