George Kyriakides - Hands-On Ensemble Learning with Python

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George Kyriakides is a Ph.D. researcher, studying distributed neural architecture search. His interests and experience include the 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.

Greg Walters has been involved with computers and computer programming since 1972. Currently, he is extremely well versed in Visual Basic, Visual Basic .NET, Python, and SQL using MySQL, SQLite, Microsoft SQL Server, Oracle, C++, Delphi, Modula-2, Pascal, C, 80x86 Assembler, COBOL, and Fortran. He is a programming trainer and has trained numerous people on many pieces of computer software, including MySQL, Open Database Connectivity, Quattro Pro, Corel Draw!, Paradox, Microsoft Word, Excel, DOS, Windows 3.11, Windows for Workgroups, Windows 95, Windows NT, Windows 2000, Windows XP, and Linux. He is currently retired and, in his spare time, is a musician and loves to cook, but he is also open to working as a freelancer on various projects.

Bhavesh Bhatt is a technology postgraduate at BITS Pilani with a keen interest in machine learning, data science, and computer vision. He currently works as a data scientist at Fractal Analytics. He has taught data science using the Python programming language to hundreds of students in the classroom. Additionally, Bhavesh hosts a machine learning-based educational YouTube channel with over 4,400 subscribers.

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Ensembling is a technique for 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. Later in the book, 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 using Python libraries such as scikit-learn and Keras to implement 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, in order to successfully implement them in real-world scenarios.