Supervised Machine Learning: Classification

A fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics

You should have familiarity with programming on a Python development environment

Describe and use decision tree and tree-ensemble models

Describe and use logistic regression models

Describe and use other ensemble methods for classification

Differentiate uses and applications of classification and classification ensembles

Use a variety of error metrics to compare and select the classification model that best suits your data

Use oversampling and undersampling as techniques to handle unbalanced classes in a data set

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