Practical Machine Learning for Data Analysis Using Python
  • Release Date : 05 June 2020
  • Publisher : Academic Press
  • Categories : Computers
  • Pages : 534 pages
  • ISBN 13 : 9780128213803
  • ISBN 10 : 0128213809
Score: 4
From 245 Ratings

Synopsis : Practical Machine Learning for Data Analysis Using Python written by Abdulhamit Subasi, published by Academic Press which was released on 05 June 2020. Download Practical Machine Learning for Data Analysis Using Python Books now! Available in PDF, EPUB, Mobi Format. 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