In the last chapter, we explored the basic blocks of TensorFlow.js to design, implement, and build a logistic regression and a linear regression model. These two models are examples of supervised learning, algorithms that use datasets made of features and labels to learn a function that maps these features to the labels.
For our next exercise, we will leave behind the topic of supervised learning and introduce its counterpart, unsupervised learning, and its quintessential algorithm, k-means.
Here, you will write a web app that trains a k-means model with ...
