Practical Machine Learning A Beginner's Guide with Ethical Insights

The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct practical, real-world solutions powered by machine learning across diverse application...

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Bibliographic Details
Main Authors: Nyamawe, Ally S, Mjahidi, Mohamedi M, Nnko, Noe E, Diwani, Salim A, Minja, Godbless G, Malyango, Kulwa
Format: eBook
Language:English
Published: Taylor & Francis 2025
Subjects:
Artificial intelligence
Automatic control engineering
choosing algorith
classification
cloud computing
Computer programming / software engineering
Computer science
Computer Technology
Computing and Information Technology
data cleaning
data collection
Electronics and communications engineering
Electronics engineering
Ethics
explainable AI
hyperparameter optimization
Nonfiction
pre-processing
programming
Programming and scripting languages: general
python
regression
responsible AI
Software Engineering
Technology, Engineering, Agriculture, Industrial processes
XAI
ISBN:9781040267639, 1040267637, 1032770295, 1003486819, 1032782161, 1040267661, 9781003486817, 9781032782164, 9781040267660, 9781032770291
Online Access:Get full text
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Summary:The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct practical, real-world solutions powered by machine learning across diverse application domains. It demonstrates the fundamental techniques involved in data collection, integration, cleansing, transformation, development, and deployment of machine learning models. This book emphasizes the importance of integrating responsible and explainable AI into machine learning models, ensuring these principles are prioritized rather than treated as an afterthought. To support learning, this book also offers information on accessing additional machine learning resources such as datasets, libraries, pre-trained models, and tools for tracking machine learning models. This is a core resource for students and instructors of machine learning and data science looking for a beginner-friendly material which offers real-world applications and takes ethical discussions into account. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
Bibliography:Electronic reproduction. Abingdon: Chapman and Hall/CRC, 2025. Requires the Libby app or a modern web browser.
ISBN:9781040267639
1040267637
1032770295
1003486819
1032782161
1040267661
9781003486817
9781032782164
9781040267660
9781032770291
DOI:10.1201/9781003486817