Supervised Machine Learning: Classification

You should have familiarity with programming on a Python development environment

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

Differentiate uses and applications of classification and classification ensembles

Describe and use logistic regression models

Describe and use decision tree and tree-ensemble models

Describe and use other ensemble methods for classification

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