Supervised Learning: Classification – W7103GSPVC

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Course Code: W7103G Brand: IBM Analytics Category: Analytics Skill Level: Intermediate Duration: 11H Modality:SPVC      Audience

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.

  Short Summary

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification  Overview

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)


1. Logistic Regression

2. K Nearest Neighbors

3. Support Vector Machines

4. Decision Trees

5. Ensemble Models

6. Modeling Unbalanced Classes


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.

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