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Ensemble Machine Learning Cookbook

You're reading from  Ensemble Machine Learning Cookbook

Product type Book
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136609
Pages 336 pages
Edition 1st Edition
Languages
Authors (2):
Dipayan Sarkar Dipayan Sarkar
Profile icon Dipayan Sarkar
Vijayalakshmi Natarajan Vijayalakshmi Natarajan
Profile icon Vijayalakshmi Natarajan
View More author details
Toc

Table of Contents (14) Chapters close

Preface 1. Get Closer to Your Data 2. Getting Started with Ensemble Machine Learning 3. Resampling Methods 4. Statistical and Machine Learning Algorithms 5. Bag the Models with Bagging 6. When in Doubt, Use Random Forests 7. Boosting Model Performance with Boosting 8. Blend It with Stacking 9. Homogeneous Ensembles Using Keras 10. Heterogeneous Ensemble Classifiers Using H2O 11. Heterogeneous Ensemble for Text Classification Using NLP 12. Homogenous Ensemble for Multiclass Classification Using Keras 13. Other Books You May Enjoy

Who this book is for

This book is designed for data scientists, machine learning developers, and deep learning enthusiasts who want to delve into machine learning algorithms to build powerful ensemble models. A working knowledge of Python programming and basic statistics is a must to help you grasp the concepts in the book.

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