Bibliothèque de L'IMIST/CNRST

Part 1. Automated machine learning. Machine learning : overview and best practices -- How automated machine learning works -- Part 2. Automated ML on Azure. Getting started with Microsoft Azure machine learning and automated ML -- Feature engineering and automated machine learning -- Deploying automated machine learning models -- Classification and regression -- Part 3. How enterprises are using automated machine learning. Model interpretability and transparency with automated ML -- Automated ML for developers -- Automated ML for everyone.

"Demand for machine learning is skyrocketing. Organizations across every industry are trying to infuse intelligence into their products and processes to delight customers and amplify business impact. But developing a good machine learning model is an iterative and time-consuming process. Automated ML makes this process easy by using machine learning to help you build models. This practical guide shows you how to apply automated ML to your data right away. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how businesses are solving real-world problems with automated ML. Data scientists and developers with some machine learning experience will learn how to use automated ML to build their models faster and more efficiently."--