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Practical Machine Learning for Data Analysis Using Python

AUTHOR Subasi, Abdulhamit
PUBLISHER Academic Press (06/07/2020)
PRODUCT TYPE Paperback (Paperback)

Description

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.

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Product Format
Product Details
ISBN-13: 9780128213797
ISBN-10: 0128213795
Binding: Paperback or Softback (Trade Paperback (Us))
Content Language: English
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Page Count: 534
Carton Quantity: 7
Product Dimensions: 7.50 x 1.08 x 9.25 inches
Weight: 2.00 pound(s)
Feature Codes: Bibliography, Index, Illustrated
Country of Origin: US
Subject Information
BISAC Categories
Computers | Artificial Intelligence - General
Dewey Decimal: 006.312
Library of Congress Control Number: 2020951343
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publisher marketing

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.

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Paperback