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  • 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

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.

  • 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
Page Count 298
Course Length 8 hours 56 minutes
ISBN 9781789612851
Date Of Publication 19 Jul 2019


George 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.

Konstantinos G. 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.