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Artificial Intelligence with Python

You're reading from   Artificial Intelligence with Python Your complete guide to building intelligent apps using Python 3.x

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781839219535
Length 618 pages
Edition 2nd Edition
Languages
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Authors (2):
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Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
Alberto Artasanchez Alberto Artasanchez
Author Profile Icon Alberto Artasanchez
Alberto Artasanchez
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Toc

Table of Contents (26) Chapters Close

Preface 1. Introduction to Artificial Intelligence 2. Fundamental Use Cases for Artificial Intelligence FREE CHAPTER 3. Machine Learning Pipelines 4. Feature Selection and Feature Engineering 5. Classification and Regression Using Supervised Learning 6. Predictive Analytics with Ensemble Learning 7. Detecting Patterns with Unsupervised Learning 8. Building Recommender Systems 9. Logic Programming 10. Heuristic Search Techniques 11. Genetic Algorithms and Genetic Programming 12. Artificial Intelligence on the Cloud 13. Building Games with Artificial Intelligence 14. Building a Speech Recognizer 15. Natural Language Processing 16. Chatbots 17. Sequential Data and Time Series Analysis 18. Image Recognition 19. Neural Networks 20. Deep Learning with Convolutional Neural Networks 21. Recurrent Neural Networks and Other Deep Learning Models 22. Creating Intelligent Agents with Reinforcement Learning 23. Artificial Intelligence and Big Data 24. Other Books You May Enjoy
25. Index

Artificial Intelligence and Big Data

In this chapter, we are going to learn what big data is and how big data technologies can be used in the context of artificial intelligence. We will discuss how big data can help accelerate machine learning pipelines. We will also discuss when it is a good idea to use big data techniques and when they are overkill, using some examples to further our understanding. We will learn about the building blocks of a machine learning pipeline that uses big data and the various challenges involved, and we will create an environment in Python to see how it works in practice. By the end of this chapter, we will have covered:

  • Big data basics
  • The three V's of big data
  • Big data as it applies to artificial intelligence and machine learning
  • A machine learning pipeline using big data
  • Apache Hadoop
  • Apache Spark
  • Apache Impala
  • NoSQL databases

Let's begin with the basics of big data.

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