Build high-impact MLAI solutions by optimizing each step. This book approaches data science solution building using a principled framework and case studies with extensive hands-on guidance. It will teach the readers optimization at each step, whether it is problem formulation or hyperparameter tuning for Deep Learning models. This book keeps the reader pragmatic and guides them toward practical solutions by discussing the essential ML concepts, including problem formulation, data preparation, and evaluation techniques. Further, the reader will be able to learn how to apply model optimization with advanced algorithms, hyperparameter tuning, and strategies against overfitting. They will also benefit from Deep Learning by optimizing models for image processing, natural language processing (NLP), and specialized applications. The reader can put theory into practice with hands-on case studies and code examples, reinforcing their understanding. With this book, the reader will be able to create high-impact, high-value MLAI solutions by optimizing each step of the solution building process, which is the ultimate goal of every Data Science professional. With this book, you will be able to approach a MLAI solution in a systematic way, optimizing each step of the solution building process. You will thus be able to create high impact, high value Machine LearningArtificial Intelligence solutions – which is the ultimate goal of every Data Science professional.