Machine Learning I

Catalog description

Introduction to probability and statistical inference used in machine learn- ing, random variables, sampling distributions, statistical signifi cance, analy- sis of variance, hypothesis testing, regression, and classifi cation. Hands-on introduction to the complete data science pipeline; Python data acquisition and cleansing, data storage and exploration, missing data treatment, fea- ture engineering, modeling, interpretation, and visualization.