BigML includes various unsupervised learning models used to gain insights into your data. These insights can help make pivotal business decisions or act as a starting point to build supervised learning models. In this course, you'll build several unsupervised learning models and analyze the results they produce.
You'll start by creating clusters from a dataset and examining how data points within a cluster share similarities. You'll move on to uncover associations in a dataset about items purchased on an e-commerce platform. Next, you'll apply topic modeling to extract the topics discussed in a collection of texts.
Following this, you'll transform a dataset containing multiple fields into a handful of principal components using Principal Component Analysis, or PCA. Finally, you'll explore the detection of anomalies in your dataset.