Supervised Learning: Regression – W7102G WBT

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Details


Course Code: W7102G

Brand: Cloud & Data Platform

Category: Cloud

Skill Level: Intermediate

Duration: 11.00H

Modality: WBT

 

 

Audience


This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression 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.

 

Short Summary


This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression

 

Overview


This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.

 

IBM Clients and Sellers – Consider this course as part of an individual or enterprise subscription service:

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)
  • IBM Learning Individual Subscription with Red Hat Learning Services (SUBR013G)

 

Topic


1. Introduction to Supervised Machine Learning and Linear Regression

2. Data Splits and Cross Validation

3. Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net

 

 

Objectives


By the end of this course you should be able to:
– Differentiate uses and applications of classification and regression in the context of supervised machine learning. 

– Describe and use linear regression models. 

– Use a variety of error metrics to compare and select a linear regression model that best suits your data. 

– Articulate why regularization may help prevent overfitting. 

– Use regularization regressions: Ridge, LASSO, and Elastic net.

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