Phase 2: Sequence Models · ~45 minutes · Python

Topic Modeling — LDA and BERTopic

LDA: documents are mixtures of topics, topics are distributions over words. BERTopic: documents cluster in embedding space, clusters are topics. Same goal, different decompositions.

What you will learn

Implement topic modeling — lda and bertopic from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Learn
Languages: Python
Prerequisites: Bag of Words, TF-IDF, and Text Representation, Word Embeddings — Word2Vec from Scratch
Time: ~45 minutes

The Problem

You have 10,000 customer support tickets, 50,000 news articles, or 200,000 tweets. You need to know what the collection is about without reading it. You do not have labeled categories. You do not even know how many categories exist.

Topic modeling answers that without supervision. Give it a corpus, get back a small set of coherent topics and, for each document, a distribution over those topics.

Two algorithmic families dominate. LDA (2003) treats each document as a mixture of latent topics and each topic as a distribution over words. Inference is Bayesian. It still ships in production where you need mixed-membership topic assignments and explainable word-level probability distributions.

BERTopic (2020) encodes documents with BERT, reduces dimensionality with UMAP, clusters with HDBSCAN, and extracts topic words via class-based TF-IDF. It wins on short text, social media, and anything where semantic similarity matters more than word overlap. One document gets one topic, which is a limitation for long-form content.

This lesson builds intuition for both and names which one to pick for a given corpus.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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