 Название: Practical Machine Learning: A Beginner's Guide with Ethical Insights
Автор: Ally S. Nyamawe, Mohamedi M. Mjahidi, Noe E. Nnko, Salim A. Diwani
Издательство: CRC Press
Год: 2025
Страниц: 226
Язык: английский
Формат: pdf (true), epub
Размер: 27.7 MB
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.
It is important to acknowledge that proficiency in computer programming is essential for developing machine learning models. Python, R, and MATLAB are widely recognized as prominent programming languages in this field. They offer comprehensive software tools, including frameworks, Integrated Development Environments (IDEs), and libraries designed to construct Machine Learning models. These languages boast large and active communities comprising developers, data scientists, researchers, and enthusiasts. These communities contribute to advancing libraries, providing assistance, and sharing knowledge and resources. They offer extensive documentation, tutorials, forums, and online courses that facilitate learning. Moreover, Python, R, and MATLAB are user-friendly, readable, and versatile, which makes them accessible to both beginners and experienced developers. This fosters a supportive and collaborative environment within these communities. The following subsections provide in-depth discussions of the programming languages commonly used in Machine Learning.
Contents:
1. Fundamentals of Machine Learning
2. Mathematics for Machine Learning
3. Data Preparation
4. Machine Learning Operations
5. Machine Learning Software and Hardware Requirements
6. Responsible AI and Explainable AI
7. Artificial General Intelligence
8. Machine Learning Step-by-Step Practical Examples
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Practical Machine Learning: A Beginner's Guide with Ethical Insights 
