This book serves as an accessible and practical introduction to data science, machine learning, and AI. Readers are guided through core machine learning methodologies covering unsupervised and supervised learning, with detailed examinations of regression, classification, and model evaluation strategies. The text introduces network science and time series analysis for modeling complex structures and temporal dependencies. Advanced topics such as decision trees, ensemble methods, neural networks, convolutional networks, and Transformers are thoroughly discussed. The book concludes with a practical overview of recommendation systems commonly used in online services. Throughout, the book emphasizes theory as well as practical understanding and application, making it ideal for readers seeking to understand the fundamentals in data-driven problem solving.