Regression Models

About the course

Regression is one of the most used model techniques for both an accurate and comprehensible prediction of non-discrete outcomes. However, despite being widespread, interpreting results and drawing correct conclusions from these models can be a challenge. Through our interactive and instructor-led exercises, you will gain a full understanding of regression models and their validation, with significant time spent on validation metrics and feature engineering. This will include linear regression, logistic regression, and regression using tree models. All of which you will get to practice in real case exercises alongside our expert trainers. Once you understand the methods and the importance of feature selection and engineering you can begin to make a real impact on your business..  

Why this is for you

Linear regression models are often regarded as simple and something anyone can build. However, in practice a lot of issues (and how to solve them) are not well-known or just discarded, leading to bad models, wrong conclusions, and diminished impact. This course aims to provide a full understanding of regression models, typical issues that you can encounter and solutions for these issues, so real impact can be achieved in your business.  

For whom

This course is aimed at all professionals, working hands-on with data and AI, from a data science or data engineering perspective. To follow this course you must have completed our Machine Learning Process (3201) course. If you are a Data Scientist and want to know more, join our program!  

What you’ll learn

1. To explain and practice linear regression models
2. To explain and practice logistic regression models
3. How to compare and contrast normal regression models with tree models
4. The criteria for selecting variables in a regression model such as R2 and p-values
5. To choose and perform the correct validation metric

Learning Goals

• Linear regression model – Proficient in building and explaining a linear regression model
• Logistic regression models – Proficient in building and explaining a logistic regression model
• Regression with tree models – Proficient in building and explaining more complex regression models using tree methods
• Feature engineering and selection – Understanding how to apply feature engineering and selection theory to regression models
• Validation metrics for regression models – Capable of choosing the right validation metric for a model and performing validation

Theory and practical use All trainings in the GAIn portfolio combine high-quality standardized training material with theory sessions from experts and hands-on experience where you directly apply the material to real-life cases. Each training is developed by top of the field practitioners which means they are full of industry examples along with practical challenges and know-how, fueling the interactive discussions during training. We believe this multi-level approach creates the ideal learning environment for participants to thrive.