Asked about Anomaly detection and working with embeddings

How would you detect anomalies without labelled examples?

An answer that works

Learn what normal looks like rather than training on the rare anomalies. Isolation forests work well on tabular data because anomalies isolate in few random splits; distance to nearest neighbours works well on embeddings.

Reading an answer is not being able to give one. The section that teaches this is Anomaly detection and working with embeddings, 4 hours of Unsupervised learning.

Learn this properly

Also asked about Unsupervised learning

You have clustered your customers into five groups. How do you know the groups are real?What does the elbow method actually tell you?What is PCA doing?Why should you not interpret distances in a t-SNE plot?How do you evaluate clustering when you have no labels?How do you choose the number of clusters?How would you find out what users are asking your AI assistant, at scale?