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

Język publikacji: angielskim
Autorzy:
George Kyriakides, Konstantinos G. Margaritis
Hands-On Ensemble Learning with Python. Build highly optimized ensemble machine learning models using scikit-learn and Keras George Kyriakides, Konstantinos G. Margaritis - okladka książki

Hands-On Ensemble Learning with Python. Build highly optimized ensemble machine learning models using scikit-learn and Keras George Kyriakides, Konstantinos G. Margaritis - okladka książki

Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
298
Dostępne formaty:
     PDF
     ePub
     Mobi

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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.

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O autorach książki

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 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.

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