Machine Learning Specialist - Supervised Learning: Regression and Classification

This course introduces you to two of the main types of modelling families of supervised Machine Learning: Regression and Classification. You start by learning how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. You then learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.

This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression and Classification techniques in a business setting.

To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

PLEASE NOTE: It may take 2-3 business days for your course access to be activated. You will receive an email from us with all necessary details.