Practical Machine Learning. Questo articolo non è disponibile.Practical Machine Learning

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.

Ally S. Nyamawe is a computer scientist with over 15 years of experience in academia. He holds a PhD in Computer Science and Technology from Beijing Institute of Technology (2020), and his research interest mainly focuses on AI applications in Software Engineering. Nyamawe is a senior lecturer in computer science at the University of Dodoma, Tanzania. Nyamawe’s recent work focused on contributing to developing AI-driven innovations that address social challenges and AI uptake for sustainable development in Africa. Nyamawe has been working on different research projects committed to fostering the application of AI for social good and leveraging coding and algorithmic skills in addressing real-world problems. Nyamawe has extensive experience in leading projects with support from the World’s renowned funders, including IDRC, Sida, UNESCO-TWAS, and the EU Erasmus+ Programme. Nyamawe actively contributes to the academic community through publications and participation in renowned conferences and international forums. He has served on the program committees for prestigious conferences, including the 37th IEEE/ACM International Conference on Automated Software Engineering, the 11th International Workshop on Software and Systems Traceability, and the 1st International Conference on the Advancements of Artificial Intelligence in African Context (AAIAC 2023). His recent recognition includes a 2022 recipient of the Seed Grant for New African Principal Investigators awarded by The World Academy of Sciences under UNESCO funding.

Mohamedi M. Mjahidi (PhD) is a Lecturer at the Department of Computer Science and Engineering (DoCSE), College of Informatics and Virtual Education (CIVE), the University of Dodoma (UDOM), Dodoma, Tanzania. He graduated from the University of Dar es Salaam (UDSM) in 2006 with a BSc. in Computer Science and completed his MSc. in Telecommunication Engineering at UDOM in 2011. He then completed his Ph.D. in Computer Engineering at the Gazi University, Ankara, Turkey, in 2020. His research interests include Artificial Intelligence, Machine Learning, and Computer and Mobile Networks. At the time of writing this book, Mjahidi is serving as the Lab and Training Coordinator for AI4D Research Lab.

Noe Elisa currently serves as the acting Director of ICT at the University of Dodoma. He is an experienced cybersecurity, Artificial Intelligence (AI), and data science researcher/engineer, boasting over eleven (11) years of experience in telecommunication, networking, software, and web applications security. He specializes in the design and implementation of AI models, particularly for detecting anomalies in computer networks. Additionally, he has expertise in using blockchain technology to develop secure and privacy-preserving decentralized systems. His PhD research at Northumbria University, UK, focused on exploiting blockchain technology and Artificial Immune Systems (AIS) to create a decentralized, secure, and privacy-preserving e-Government system for enforcing data protection and trust. During his master's degree studies in India in 2014, he received professional training in ethical hacking, Linux server administration, Android mobile application development, CCNA, and web application programming in JAVA. One of his notable contributions to AI and academic research is the development of a new general-purpose multi-class classifier based on the Dendritic Cell Algorithm (McDCA), which is currently under review by the IEEE Transactions on Neural Networks and Learning Systems journal. His current research and practical endeavors focus on leveraging African-origin datasets to develop AI-powered solutions that address privacy concerns related to data breaches and unlawful access to personal information.

Salim Diwani is a highly skilled and experienced lecturer at the University of Dodoma, specializing in machine learning and artificial intelligence for more than twelve years. Diwani, acknowledged as a senior expert in various fields, has made noteworthy contributions to both academia and practical applications. At the University of Dodoma, he has fostered a vibrant atmosphere in which students have developed and put into practice cutting-edge artificial intelligence solutions across various industries, including agriculture and healthcare. Diwani not only holds a position at the institution but also acts as the coordinator for the Healthcare Coordination Unit at AI4D Research Lab. He is responsible for supervising a group of committed professionals who are dedicated to utilizing AI technologies to tackle urgent healthcare issues. Diwani's leadership includes the responsibility of hosting graduate students supported by AI4D Research Lab. Diwani is leading the Healthcare Coordination Unit in developing AI solutions specifically designed for the local requirements of the Anglophone region in Africa. Diwani and his colleagues are closely collaborating with the Ministry of Health in the Government of Tanzania to build an AI policy in the health sector. This effort aims to establish a favorable setting for the acceptance and assimilation of AI technologies in healthcare. Diwani and his colleagues are leading the way in utilizing AI to transform healthcare delivery and address intricate healthcare issues in Africa and beyond.

Godbless G. Minja is an Assistant Lecturer in the Department of Computer Science and Engineering (DoCSE) at the University of Dodoma (UDOM) in Tanzania. He completed a B.Sc. in Computer Science at the University of Dar es Salaam (UDSM) in Tanzania and an M.Sc. in Cyber Security at the University of Birmingham in the United Kingdom (UK). He is currently pursuing a Ph.D. in Information and Communication Science and Engineering (ICSE) at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Tanzania.

Kulwa Malyango is a research assistant and software developer at the AI4D Research Lab. He has a degree in computer science from the University of Dodoma in Tanzania. His research interests are in the application of artificial intelligence in the digital economy. Currently pursuing a master's degree in computer science, Kulwa is expanding his expertise in software development and artificial intelligence by working with esteemed researchers at the AI4D Research Lab. His main goal is to contribute meaningful research and practical solutions that can positively impact the digital economy, both locally and globally. He envisions a future where AI technologies are responsibly integrated into various sectors to improve people's lives.