• Online, Self-Paced
Course Description

Unsupervised techniques such as clustering and recommendation systems can discover patterns in unlabeled data. These models extract structure in the x-variables or features present in the data.

In this course, you will work with two unsupervised learning methods, clustering and recommendation systems. You will explore how clustering algorithms use only the x-variables or features in your data to group data into logical clusters. Then you will discover the basic concepts behind recommendation systems, which take in past user interactions with products and use that to recommend new products to users.

Next, you will train a clustering model using k-means clustering on your data and evaluate how the clusters differ. You will use hyperparameter tuning to find the best number of clusters on your dataset.

Finally, you will train a recommendations engine using collaborative filtering and use that to make movie recommendations to users based on their past preferences and the preferences of other users.

Learning Objectives

{"discover the key concepts covered in this course"}

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture