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Practical Machine Learning for Data Analysis Using Python by Abdulhamit Subasi (University of Albany) at Abbey's Bookshop,
Practical Machine Learning for Data Analysis Using Python
— —
Abdulhamit Subasi (University of Albany)

Practical Machine Learning for Data Analysis Using Python

Abdulhamit Subasi (University of Albany)

$245.95

Paperback

Not in-store but you can order this
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English
Academic Press Inc
07 June 2020
Data capture & analysis; Machine learning
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.
By:  
Imprint:   Academic Press Inc
Country of Publication:   United States
Dimensions:   Height: 235mm,  Width: 191mm, 
Weight:   1.110kg
ISBN:   9780128213797
ISBN 10:   0128213795
Pages:   534
Publication Date:  
Audience:   College/higher education ,  Primary
Format:   Paperback
Publisher's Status:   Active
1. Introduction 2. Data preprocessing3. Machine learning techniques4. Classification examples for healthcare5. Other classification examples6. Regression examples7. Clustering examples

Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Analysis and Security. 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.

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