In this chapter, we'll look at how to build and evaluate an unsupervised model. We'll also look at semi-supervised learning, the difference between unsupervised and semi-supervised learning, how to build a semi-supervised model, and how to make predictions using a semi-supervised model.
In this chapter, we'll cover the following topics:
- Working with k-means clustering
- Evaluating a clustering model
- Distance matrix formation using cosine similarity
- The difference between unsupervised and semi-supervised learning
- Self-training and co-training machine learning models
- Making predictions with semi-supervised machine learning models
