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Supervised Learning: Classification – w7103gspl

Course #: w7103gspl

Duration: 3.2 Hours

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)

Objectives

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.

Audience

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

Prerequisites

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.

Topics

1. Logistic Regression

2. K Nearest Neighbors

3. Support Vector Machines

4. Decision Trees

5. Ensemble Models

6. Modeling Unbalanced Classes

Contact us regarding the training