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Hands-On Ensemble Learning with Python

You're reading from Hands-On Ensemble Learning with Python Build highly optimized ensemble machine learning models using scikit-learn and Keras

Product type Paperback

Published in Jul 2019

Publisher Packt

ISBN-13 9781789612851

Length 298 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Konstantinos G. Margaritis

George Kyriakides

View More author details

Table of Contents (20) Chapters

Preface

1. Section 1: Introduction and Required Software Tools

2. A Machine Learning Refresher FREE CHAPTER

3. Getting Started with Ensemble Learning

4. Section 2: Non-Generative Methods

5. Voting

6. Stacking

7. Section 3: Generative Methods

8. Bagging

9. Boosting

10. Random Forests

11. Section 4: Clustering

12. Clustering

13. Section 5: Real World Applications

14. Classifying Fraudulent Transactions

15. Predicting Bitcoin Prices

16. Evaluating Sentiment on Twitter

17. Recommending Movies with Keras

18. Clustering World Happiness

19. Another Book You May Enjoy

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Section 2: Non-Generative Methods

In this section, we will cover the simplest methods of ensemble learning.

This section comprises the following chapters:

Chapter 3, Voting
Chapter 4, Stacking

The rest of the chapter is locked

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Authors (2)

Kyriakides

Kyriakides

George Kyriakides is a Ph.D. researcher, studying distributed neural architecture search. His interests and experience include automated generation and optimization of predictive models for a wide array of applications, such as image recognition, time series analysis, and financial applications. He holds an M.Sc. in computational methods and applications, and a B.Sc. in applied informatics, both from the University of Macedonia, Thessaloniki, Greece.

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Margaritis

Margaritis

Konstantinos G. Margaritis has been a teacher and researcher in computer science for more than 30 years. His research interests include parallel and distributed computing as well as computational intelligence and machine learning. He holds an M.Eng. in electrical engineering (Aristotle University of Thessaloniki, Greece), as well as an M.Sc. and a Ph.D. in computer science (Loughborough University, UK). He is a professor at the Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece.

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