The Second Edition of the Machine Learning is a substantial upgrade, reflecting the rapid evolution of the field in recent years. It is intended as an introductory textbook employing a clear algorithmic approach and numerical examples to explain machine learning concepts. The content is designed for undergraduate and postgraduate students in Computer Science, Information Technology and related engineering disciplines. It is also suitable for learners in data science, data analytics and data mining. A new chapter has been added in this edition to deepen readers' understanding of the mathematical foundations essential for machine learning. Two additional chapters introduce data preparation and feature engineering, along with exploratory data analysis. The book also offers supplementary online theoretical material and a laboratory manual, accessible through QR codes placed within the relevant sections. The laboratory component includes 25 Python-based experiments and case studies designed to provide hands-on experience with machine learning techniques.