Dask has revolutionized parallel computing for Python, empowering data scientists to accelerate their workflows. This comprehensive guide unravels the intricacies of Dask to help you harness its capabilities for machine learning and data analysis. Dedicated chapters explore scaling regression, classification, hyperparameter tuning, feature engineering, and more with clear examples. This book places special emphasis on practical use cases related to scalability and distributed computing. With this book, you'll gain practical skills to: Packed with hands-on examples and expert insights, this book provides the complete toolkit to harness Dask's capabilities. It will empower Python programmers, data scientists, and machine learning engineers to achieve faster workflows and operationalize parallel computing.