Gray cover of Machine Learning with Ridge and Lasso Regression. This template resource is from 365 Data Science.

Machine Learning with Ridge and Lasso Regression

Ivan Manov
Course Author
One of the most common problems every data scientist faces when training machine learning models is overfitting—overcomplicating the model with irrelevant data, thereby reducing the model’s predictive and classification capabilities. This is where the regularization techniques -ridge and lasso come to the rescue, as they simplify the model and remove all the data noise. In these free pdf course notes, we will cover the basic concepts behind regression analyses, ridge vs lasso regression, cross validation for choosing a tuning parameter and relevant metrics for evaluating the model’s performance.

Who is it for

Machine Learning Engineers, Data Scientists, Data Engineers and anyone who is interested in building high-performing machine learning models will benefit from the information provided in these free course notes.

How it can help you

Whenever you are testing a machine learning algorithm an inevitable problem you will encounter is overfitting and multicollinearity which greatly reduce the predictive accuracy of the model. By studying these course notes you will know how to combat this issue and evaluate the effectiveness of the solution.

Machine Learning with Ridge and Lasso Regression