AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this course, you will learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this course, you will have a clearer understanding of the different aspects of automated Machine Learning, and you will be able to incorporate automation tasks using practical datasets. You can leverage your learning from this course to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.
