Introduction to Machine Learning: Regression Models

This workshop focuses on regression models to provide participants with a foundational understanding of machine learning concepts, techniques, and tools used for linear and nonlinear regression. Through a combination of lectures and hands-on exercises, participants will gain practical experience with regression algorithms, one of the most popular machine-learning techniques. The workshop begins with an overview of regression, exploring its various types and applications and continues with training regression models, interpreting the results, and making predictions using real-world datasets. Additionally, participants will gain insights into advanced topics such as regularization and feature selection. By the end of this workshop, you will have a solid understanding of regression models and be familiar with popular Python libraries and tools to implement them.

In this workshop, we will use cloud-based platforms, so you don’t need to have Python installed. Please make sure that you have a Google Colaboratory (https://colab.research.google.com/) account. This workshop will involve hands-on exercises that require the use of programming tools and libraries commonly used in machine learning, such as Python and Scikit-learn. As such, prior familiarity with Python programming is recommended for participants to fully benefit from the practical component of the workshop.

Learning objectives

At the end of this workshop, you will be able to:

1. Understand regression models and their applications in machine learning.
2. Train regression models for real-world datasets.
3. Interpret and analyze regression model results.

Schedule

Slides

Find the workshop slides below or open it in a new tab:

Other workshops in the series

Workshop 2- Classification and Clustering: Click here
Workshop 3- Neural Networks: Click here

Resources

• Colab Notebook:

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