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Practical machine learning for data analysis using python /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Subasi, Abdulhamit (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2020
Temas:
Acceso en línea:Texto completo

MARC

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020 |a 9780128213803  |q (electronic bk.) 
020 |a 0128213809  |q (electronic bk.) 
020 |z 9780128213797 
020 |z 0128213795 
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100 1 |a Subasi, Abdulhamit,  |e author. 
245 1 0 |a Practical machine learning for data analysis using python /  |c Abdulhamit Subasi. 
264 1 |a London :  |b Academic Press,  |c 2020 
264 4 |c �2020 
300 |a 1 online resource (xiii, 520 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
520 8 |a 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. 
650 0 |a Machine learning. 
650 0 |a Data mining. 
650 0 |a Python (Computer program language) 
650 2 |a Data Mining  |0 (DNLM)D057225 
650 6 |a Apprentissage automatique.  |0 (CaQQLa)201-0131435 
650 6 |a Exploration de donn�ees (Informatique)  |0 (CaQQLa)201-0300292 
650 6 |a Python (Langage de programmation)  |0 (CaQQLa)201-0351937 
650 6 |a Livres num�eriques.  |0 (CaQQLa)201-0160798 
650 7 |a e-books.  |2 aat  |0 (CStmoGRI)aat300265554 
650 7 |a Data mining.  |2 fast  |0 (OCoLC)fst00887946 
650 7 |a Machine learning.  |2 fast  |0 (OCoLC)fst01004795 
650 7 |a Python (Computer program language)  |2 fast  |0 (OCoLC)fst01084736 
655 4 |a book. 
856 4 0 |u https://sciencedirect.uam.elogim.com/science/book/9780128213797  |z Texto completo