Machine Learning

Positions BERTopic as the modern topic-modeling stack combining transformer embeddings, UMAP, HDBSCAN, and class-based TF-IDF to produce human-readable topics with representative documents. Warns that unsupervised output is always a hypothesis to be validated, not a discovered truth, and lists representation, objective, evaluation, and deployment gaps. Builds a scikit-learn KMeans baseline on a synthetic subscription customer dataframe, contrasting Euclidean and cosine distance, scaling effects, and the curse of dimensionality that drives feature selection and embeddings.

Why K-Means Is Still the Default (And When to Skip It). At 9:17 on a Monday morning, a dashboard can show five tidy groups and still tell a completely false story. The interesting part of unsupervised learning isn't making colored dots; it's discovering useful structure, then proving that result survives

Unsupervised Learning in 2026: When It Still Beats Supervised At 9:17 on a Monday morning, a dashboard can show five tidy groups and still tell a completely false story. The Core Idea Unsupervised Learning learns structure without receiving the final answer for every row. When to Use This (And When Not To) Use unsupervised learning when labels are unavailable, stale, expensive, or unable to express the question.

Unsupervised feature learning addresses labeled data scarcity by learning representations from unlabeled data. Approaches include autoencoders (compress and reconstruct to extract bottleneck features — also doubles as anomaly detection via reconstruction error), variational autoencoders (Gaussian prior on latent space for smooth generation), and contrastive learning methods like SimCLR (identify similar vs different samples). The healthcare deterioration detector story: 8,000 labeled events weren't enough, but pre-training an encoder via contrastive predictive coding on millions of unlabeled vital-sign windows beat every supervised baseline. Try a denoising autoencoder on your largest unlabeled pool first.