This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. 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.

IBM Customers and Sellers: If you are interested in this course, consider purchasing it as part of one of these Individual or Enterprise Subscriptions:

• IBM Learning for Data and AI Individual Subscription (SUBR022G)
• IBM Learning for Data and AI Enterprise Subscription (SUBR004G)
• IBM Learning Individual Subscription with Red Hat Learning Services (SUBR023G)

By the end of this course you should be able to: 

- 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.

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.

1. Logistic Regression

2. K Nearest Neighbors

3. Support Vector Machines

4. Decision Trees

5. Ensemble Models

6. Modeling Unbalanced Classes