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

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781839219535

Length 618 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Authors (2):

Prateek Joshi

Alberto Artasanchez

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Table of Contents (26) Chapters

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

Big data and machine learning

Big data technologies are leveraged successfully by technology companies around the world. Today's enterprises understand the power of big data, and they realize that it can be even more powerful when used in conjunction with machine learning.

Machine learning systems coupled with big data technology help businesses in a multitude of ways including managing, analyzing, and using the captured data far more strategically than ever before.

As companies capture and generate ever increasing volumes of data, this presents both a challenge and a great opportunity. Fortunately, these two technologies complement each other symbiotically. Businesses are constantly coming up with new models that increase the computational requirements of the resulting workloads. New advances in big data enable and facilitate the processing of these new use cases. Data scientists are seeing that current architectures can handle this increased workload. They are therefore...