Practical Machine Learning for Data Analysis Using Python

Subasi PhD., Abdulhamit

ISBN 10: 0128213795 / ISBN 13: 9780128213797

Published by Elsevier, 2020

About this Item

pp. 370.

Seller Inventory # 369167599

Bibliographic Details

• Title: Practical Machine Learning for Data Analysis Using Python
• Publisher: Elsevier
• Publication Date: 2020
• Language: English
• ISBN 10: 0128213795
• ISBN 13: 9780128213797
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.

• Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas
• Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data
• Explores important classification and regression algorithms as well as other machine learning techniques
• Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features

About the Author

Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Processing. His extensive expertise in applying machine learning across diverse domains is evident in his numerous contributions, including the authorship of multiple book chapters, as well as the publication of a substantial body of research in esteemed journals and conferences. His career has spanned various prestigious institutions, including the Georgia Institute of Technology in Georgia, USA, where he served as a dedicated researcher. In recognition of his outstanding research contributions, Subasi received the prestigious Queen Effat Award for Excellence in Research in May 2018. His academic journey includes a tenure as a Professor of computer science at Effat University in Jeddah, Saudi Arabia, from 2015 to 2020. Since 2020, he has assumed the role of Professor of medical physics at the Faculty of Medicine, University of Turku in Turku, Finland