Learning Outcomes

In this free course, you will learn how to:

• Define the main steps in a machine learning workflow
• Apply scikit-learn tools to prepare data and build models
• Create regression models to predict numerical values from datasets
• Classify data into categories using suitable classification models
• Compare machine learning models using suitable evaluation results
• Calculate common measures to assess model performance
• Select suitable models and settings for different machine learning tasks
• Apply clustering methods to group similar data points
• Identify useful features and reduce unnecessary data dimensions
• Discuss pipelines that combine data preparation and model training
• Arrange the steps of an end-to-end machine learning project
• Generate a complete machine learning solution from data preparation to model evaluation