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- Author: Amar Sahay
- Publisher: Business Expert Press
- ISBN-10: 1637427484
- ISBN-13: 9781637427484
- Format: 15.2 x 22.9 x 1.4 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
Machine Learning Fundamentals provides a comprehensive overview of data science, emphasizing machine learning (ML). This book covers ML fundamentals, processes, and applications, that are used as industry standards. Both supervised and unsupervised learning ML models are discussed.
Topics include data collection and feature engineering techniques as well as regression, classification, neural networks (deep learning), and clustering. Motivated by the success of ML in various fields, this book is designed for a wide audience coming from various disciplines such as engineering, IT, or business and is suitable for those getting started with ML for the first time.
This text can also serve as the main or supplementary text in any introductory data science course from any discipline, offering real-world applications and tools in all areas.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 23d.07:58:21
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Amar Sahay
- Publisher: Business Expert Press
- ISBN-10: 1637427484
- ISBN-13: 9781637427484
- Format: 15.2 x 22.9 x 1.4 cm, minkšti viršeliai
- Language: English English
Machine Learning Fundamentals provides a comprehensive overview of data science, emphasizing machine learning (ML). This book covers ML fundamentals, processes, and applications, that are used as industry standards. Both supervised and unsupervised learning ML models are discussed.
Topics include data collection and feature engineering techniques as well as regression, classification, neural networks (deep learning), and clustering. Motivated by the success of ML in various fields, this book is designed for a wide audience coming from various disciplines such as engineering, IT, or business and is suitable for those getting started with ML for the first time.
This text can also serve as the main or supplementary text in any introductory data science course from any discipline, offering real-world applications and tools in all areas.
Reviews