Hands-On Ensemble Learning with Python

Hands-On Ensemble Learning with Python

Build highly optimized ensemble machine learning models using scikit-learn and Keras


This eBook includes

Formats : PDF, EPUB, MOBI (Downlodable)

Pages : 298

ISBN : 9781789617887

Language : English

About the Book

Book description

Combine popular machine learning techniques to create ensemble models using Python

Key Features

  • 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

Book 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 on the basic theory but also the application of various 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. Furthermore, 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

Who this book is for

This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.

Hands-On Ensemble Learning with Python

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Author Details

Packt Publishing

Packt Publishing

Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.

Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.

As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.

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