Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science

Soly Mathew Biju (u. a.)

ISBN 10: 1668486970 / ISBN 13: 9781668486979

Published by IGI Global, 2023

About this Item

Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science | Soly Mathew Biju (u. a.) | Taschenbuch | Englisch | 2023 | IGI Global | EAN 9781668486979 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 127710279

Bibliographic Details

• Title: Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science
• Publisher: IGI Global
• Publication Date: 2023
• Language: English
• ISBN 10: 1668486970
• ISBN 13: 9781668486979
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 18D millimeters 254 millimeters width by 178 millimeters height by 18 millimeters depth
• Weight: 619 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The world is approaching a point where big data will start to play a beneficial role in many industries and organizations. Today, analyzing data for new insights has become an everyday norm, increasing the need for data analysts to use efficient and appropriate tools to provide quick and valuable results to clients. Existing research in the field currently lacks a full coverage of all essential algorithms, leaving a knowledge void for practical implementation and code in Python with all needed libraries and links to datasets used. Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science serves as a one-stop book to help emerging data scientists gain hands-on skills needed through real-world data and completely up-to-date Python code. It covers all the technical details, from installing the needed software to importing libraries and using the latest data sets; deciding on the right model; training, testing, and evaluating the model; and including NumPy, Pandas, and matplotlib. With coverage on various machine learning algorithms like regression, linear and logical regression, classification, support vector machine (SVM), clustering, k-nearest neighbor, market basket analysis, Apriori, k-means clustering, and visualization using Seaborne, it is designed for academic researchers, undergraduate students, postgraduate students, executive education program leaders, and practitioners.