- We will send in 10–14 business days.
/images/books/2821548/9780128213797.jpg)
- Author: Abdulhamit Subasi
- Publisher: Academic Press
- ISBN-10: 0128213795
- ISBN-13: 9780128213797
- Format: 19.1 x 23.5 x 2.7 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Practical Machine Learning for Data Analysis Using Python (e-book) (used book) | bookbook.eu
Reviews
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 18d.13:40:27
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Abdulhamit Subasi
- Publisher: Academic Press
- ISBN-10: 0128213795
- ISBN-13: 9780128213797
- Format: 19.1 x 23.5 x 2.7 cm, softcover
- Language: English English
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.
Reviews