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Practical Data Analysis

You're reading from   Practical Data Analysis Pandas, MongoDB, Apache Spark, and more

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Product type Paperback
Published in Sep 2016
Publisher
ISBN-13 9781785289712
Length 338 pages
Edition 2nd Edition
Languages
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Authors (2):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
Dr. Sampath Kumar Dr. Sampath Kumar
Author Profile Icon Dr. Sampath Kumar
Dr. Sampath Kumar
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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started 2. Preprocessing Data FREE CHAPTER 3. Getting to Grips with Visualization 4. Text Classification 5. Similarity-Based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Diseases with Cellular Automata 10. Working with Social Graphs 11. Working with Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with Jupyter and Wakari 15. Understanding Data Processing using Apache Spark

Machine learning

Machine learning (ML) is the study of computer algorithms to learn how to react in a certain situation or recognize patterns. According to Arthur Samuel (1959):

"Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed".

ML has a large amount of algorithms generally split into three groups depending how the algorithms are training. They are as follows:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

The relevant number of algorithms is used throughout the book and they are combined with practical examples, leading the reader through the process from the initial data problem to its programming solution.

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