Explore a 30-minute EuroPython Conference talk on building a Naive Bayes text classifier using scikit-learn. Learn about the algorithm's simplicity and effectiveness in classifying large, sparse datasets like text documents. Discover preprocessing techniques such as text normalization and feature extraction. Follow along as the speaker demonstrates model construction using the spam/ham YouTube comment dataset from the UCI repository. Gain insights into the Naive Bayes algorithm's history, advantages, and disadvantages. Dive into practical examples, equations, and implementation steps, including dataset loading, train/test splitting, and feature extraction using bag-of-words and TF-IDF approaches. Conclude with techniques for model evaluation and parameter tuning through Laplace smoothing.