
Explore the basics of regression in machine learning, compare ridge and lasso models side by side, and learn to interpret data and implement them hands-on.
Explore linear regression foundations in supervised learning and compare ridge (L2) and lasso (L1) regularization, then learn training–testing splits and common scoring measures like r squared and mean squared error.
Learn to select the output and independent variables, perform a train-test split, and evaluate ridge and lasso models with correlation heat maps and metrics like r-squared and mean absolute error.
Learn how to upload and prep a dataset in Google Colab by linking Kaggle datasets, uploading the Kaggle JSON API key, and preparing features and target variable for train-test split.
Implement ridge and lasso regression models using scikit-learn, tune alpha, and evaluate with MSE, MAE, and R2 on train and test sets, then compare results.
Compare lasso and ridge models by adjusting alpha, merge results into a table, and plot mean squared error and mean absolute error to conclude that lasso outperforms ridge on dataset.
In this course, you will begin by gaining a comprehensive understanding of linear regression, ridge regression, and lasso regression. These foundational concepts in machine learning are essential for anyone interested in data analysis and predictive modeling. You will learn how these models work and how they manipulate datasets to establish a correlation between independent variables and the dependent variable. This understanding will enable you to forecast outcomes based on the input data effectively.
As you progress, you will have the opportunity to implement both ridge and lasso regression models within your Google Colab project. This hands-on experience will allow you to apply theoretical knowledge in a practical setting, enhancing your learning process and boosting your confidence in using these models. After implementing the models, you will evaluate their performance by scoring them and comparing the results. This comparison will help you determine which model performs better in predicting outcomes based on your dataset.
By the end of this course, you should have a solid foundation in regression machine learning models. You will be equipped with the skills necessary to apply these techniques in real-life scenarios, empowering you to make informed data-driven decisions and insights in various contexts. Furthermore, applying these models to real-life scenarios will give you a great insight on recent data-analytics jobs.