Automated Machine Learning (AutoML) refers to the process of automating the end-to-end process of applying machine learning to real-world problems. It involves developing algorithms and systems that automatically handle tasks such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation. AutoML aims to simplify and accelerate the machine learning workflow, making it accessible to users without extensive expertise in data science or machine learning. Techniques used in AutoML include meta-learning, Bayesian optimization, and evolutionary algorithms to efficiently search and optimise models and their parameters. AutoML reduces the manual effort required to build and deploy machine learning models, thereby democratising access to powerful predictive tools across various industries. AutoML continues to evolve with advancements in algorithm design and computational efficiency, driving innovation in machine learning applications. This book provides comprehensive insights into the field of automated machine learning. The topics included in this book on artificial intelligence are of utmost significance and bound to provide incredible insights to readers. It is an essential guide for both academicians and those who wish to pursue this discipline further.