This course, offered by IBM, introduces aspiring data scientists to the world of supervised Machine Learning through Regression. Throughout the course, students will delve into the intricate details of training regression models, predicting continuous outcomes, and utilizing error metrics to compare and select the most suitable model for their data.
The course covers topics such as supervised machine learning, linear regression, data splits, polynomial regression, cross-validation, and regularization techniques including Ridge, LASSO, and Elastic Net. Aspiring data scientists will also gain insight into the bias-variance trade-off and the importance of regularization in preventing overfitting.
Upon completion, learners will be equipped with the skills and knowledge necessary to differentiate uses and applications of classification and regression, articulate why regularization may help prevent overfitting, and use a variety of error metrics to compare and select the best linear regression model for their data.
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This course consists of six modules that cover supervised machine learning, linear regression, data splits, polynomial regression, cross-validation, and regularization techniques including Ridge, LASSO, and Elastic Net.
This module introduces aspiring data scientists to supervised machine learning and linear regression. Students will gain insight into the types of machine learning, regression, and classification examples, as well as an introduction to linear regression. They will also have the opportunity to engage in lab exercises and quizzes to reinforce their learning.
In this module, students will delve into the concepts of data splits and polynomial regression. They will learn about training and test splits, polynomial regression, and engage in lab exercises and quizzes to strengthen their understanding of these concepts.
Module 3 focuses on cross-validation, a crucial aspect of model validation in machine learning. Students will gain practical experience in cross-validation through demo labs and practice exercises, ensuring they have a solid grasp of this essential technique.
Students will explore the bias-variance trade-off and various regularization techniques, including Ridge, LASSO, and Elastic Net. The module includes practical demonstrations and quizzes to reinforce learning and help students understand the importance of regularization in preventing overfitting.
This module delves deeper into the details of regularization, providing students with a comprehensive understanding of this crucial topic. Students will engage in demo labs and practice exercises to solidify their knowledge of regularization techniques.
The final project module offers students the opportunity to apply their knowledge and skills by working on a hands-on final project. This project allows them to demonstrate their ability to apply supervised machine learning regression techniques in a business setting.
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