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

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A Machine Learning Refresher

Machine learning is a sub field of artificial intelligence (AI) focused on the aim of developing algorithms and techniques that enable computers to learn from massive amounts of data. Given the increasing rate at which data is produced, machine learning has played a critical role in solving difficult problems in recent years. This success was the main driving force behind the funding and development of many great machine learning libraries that make use of data in order to build predictive models. Furthermore, businesses have started to realize the potential of machine learning, driving the demand for data scientists and machine learning engineers to new heights, in order to design better-performing predictive models.

This chapter serves as a refresher on the main concepts and terminology, as well as an introduction to the frameworks that will be used...

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

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