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Hands-On Ensemble Learning with Python
Hands-On Ensemble Learning with Python

Hands-On Ensemble Learning with Python: Build highly optimized ensemble machine learning models using scikit-learn and Keras

By George Kyriakides , Konstantinos G. Margaritis
£25.99 £7.99
Book Jul 2019 298 pages 1st Edition
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eBook
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Hands-On Ensemble Learning with Python

Section 1: Introduction and Required Software Tools

This section is a refresher on basic machine learning concepts and an introduction to ensemble learning. We will have an overview of machine learning and various concepts pertaining to it, such as train and test sets, supervised and unsupervised learning, and more. We will also learn about the concept of ensemble learning.

This section comprises the following chapters:

  • Chapter 1, A Machine Learning Refresher
  • Chapter 2, Getting Started with Ensemble Learning
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Key benefits

  • Implement ensemble models using algorithms such as random forests and AdaBoost
  • Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model
  • Explore real-world data sets and practical examples coded in scikit-learn and Keras

Description

Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed with the basic theory but also the application of different ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. In addition to this, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.

What you will learn

Implement ensemble methods to generate models with high accuracy Overcome challenges such as bias and variance Explore machine learning algorithms to evaluate model performance Understand how to construct, evaluate, and apply ensemble models Analyze tweets in real time using Twitter s streaming API Use Keras to build an ensemble of neural networks for the MovieLens dataset

Product Details

Country selected

Publication date : Jul 19, 2019
Length 298 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789612851
Vendor :
Google
Category :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Buy Now

Product Details


Publication date : Jul 19, 2019
Length 298 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789612851
Vendor :
Google
Category :

Table of Contents

20 Chapters
Preface Chevron down icon Chevron up icon
1. Section 1: Introduction and Required Software Tools Chevron down icon Chevron up icon
2. A Machine Learning Refresher Chevron down icon Chevron up icon
3. Getting Started with Ensemble Learning Chevron down icon Chevron up icon
4. Section 2: Non-Generative Methods Chevron down icon Chevron up icon
5. Voting Chevron down icon Chevron up icon
6. Stacking Chevron down icon Chevron up icon
7. Section 3: Generative Methods Chevron down icon Chevron up icon
8. Bagging Chevron down icon Chevron up icon
9. Boosting Chevron down icon Chevron up icon
10. Random Forests Chevron down icon Chevron up icon
11. Section 4: Clustering Chevron down icon Chevron up icon
12. Clustering Chevron down icon Chevron up icon
13. Section 5: Real World Applications Chevron down icon Chevron up icon
14. Classifying Fraudulent Transactions Chevron down icon Chevron up icon
15. Predicting Bitcoin Prices Chevron down icon Chevron up icon
16. Evaluating Sentiment on Twitter Chevron down icon Chevron up icon
17. Recommending Movies with Keras Chevron down icon Chevron up icon
18. Clustering World Happiness Chevron down icon Chevron up icon
19. Another Book You May Enjoy Chevron down icon Chevron up icon

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