Machine Learning for Scientists & Engineers

Based on the Enthought Academy course of the same name, Machine Learning for Scientists & Engineers provides scientists and engineers with a practical introduction to classical machine learning using Scikit-learn.  

In this class you'll learn to build supervised learning models, starting with Regression and then moving into Classification.  A major focus of the class is effective Feature Engineering, with hands-on practice and live demos of Univariate, Bivariate, and Multivariate analysis for feature engineering.  Using concrete examples (including, literally, a multi-part guided demo using data from a study of variation in concrete strength with composition) we lay a solid foundation for building machine learning models and workflows. There are many exercises and demos, and you'll be encouraged to follow along with your own copies of the Jupyter Notebooks and ask questions to make sure you are comfortable with the material.  You'll leave not only with knowledge of the fundamentals of Machine Learning, but also with a rich collection of annotated examples and evaluation tools to use in your own future projects.

Click here for a full description and syllabus.