2027 Unsupervised Learning in Python: Cluster Analysis & PCA

About this course

This course contains the use of artificial intelligence. The course outline, the explanations, the tool choices and every measured number are my own.

Learn unsupervised machine learning in Python on real data that has no labels. In this hands-on course you use scikit-learn, UMAP, PyOD, Keras, PyTorch, aeon, implicit, BERTopic and Ollama for cluster analysis, PCA and other dimensionality reduction, anomaly detection, credit card fraud detection, autoencoders, semi-supervised learning, time series clustering, recommendation systems and text clustering with a local LLM.Most real data comes without an answer key. Nobody has marked which customers belong together, which transactions are fraud, which heartbeats look alike, which movie a user will want next or what a pile of bank messages is about. Unsupervised learning is how you find structure in data like this: you compress it, group it, rank what looks unusual and learn representations that other models build on, and then you decide what to do with it.

Unsupervised Learning in Python teaches each method one step at a time. You start with a map of where unsupervised learning sits in machine learning, with no code. Then you work through seven code sections of notebooks on real data: handwritten digits, credit card transactions, wholesale customers, heartbeat recordings, MovieLens movie watches and bank customer messages. New sections keep coming to the course.

What makes this course different

Fraud detection without labels. You score 284,807 real card transactions, of which only 0.17% are fraud, with PCA reconstruction error, Isolation Forest, Local Outlier Factor and ECOD, then with autoencoders. You grade every score with average precision, the precision-recall curve and a daily review budget, because plain accuracy fails on rare fraud.

Cluster analysis, start to finish.

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