After data has been prepared, the next step in the machine learning lifecycle is model training and evaluation. In this course, you will focus on the model training and evaluation process for supervised learning models and explore a few supervised learning algorithms that are commonly used. You will be introduced to the model training for two popular supervised learning algorithms: k-nearest neighbors (KNN) and decision trees (DT), exploring their applicability to classification problems. You will practice creating your own machine learning models using a popular Python package for machine learning called scikit-learn. By the end of this course, you will have new, applicable skills in training common ML models.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Machine Learning Foundations
• Managing Data in Machine Learning