Practical Machine Learning for Data Analysis Using Python

Erscheinungsjahr 2020
ISBN: 978-0-12-821379-7
Verlag: ACADEMIC PRESS

Buch, Englisch, 534 Seiten, Format (B × H): 150 mm x 210 mm, Gewicht: 1110 g

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.

Zielgruppe

Researchers and graduate students in biomedical engineering, electrical and electronics engineering, computer science, biomedical informatics, as well as professionals in data science and data analytics

Autoren/Hrsg.

Subasi, Abdulhamit

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

1. Introduction 2. Data preprocessing3. Machine learning techniques4. Classification examples for healthcare5. Other classification examples6. Regression examples7. Clustering examples

Über Autor(innen)

Subasi, Abdulhamit
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.