<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Conditional Probability &amp; Bayes on DATATWEETS</title><link>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/</link><description>Recent content in Conditional Probability &amp; Bayes on DATATWEETS</description><generator>Hugo</generator><language>en</language><copyright>Copyright (c) 2025 Datatweets</copyright><lastBuildDate>Sat, 27 Jun 2026 09:00:00 +0200</lastBuildDate><atom:link href="https://datatweets.com/courses/statistics/conditional-probability-and-bayes/index.xml" rel="self" type="application/rss+xml"/><item><title>Lesson 1 - Conditional Probability Fundamentals</title><link>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/lesson-1-conditional-probability-fundamentals/</link><pubDate>Fri, 14 Nov 2025 09:00:00 +0200</pubDate><guid>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/lesson-1-conditional-probability-fundamentals/</guid><description>Meet conditional probability — the probability of A given B — and see how conditioning on one fact changes a probability, why P(A|B) is not P(B|A), and how it connects to independence.</description></item><item><title>Lesson 2 - Conditional Probability: Intermediate</title><link>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/lesson-2-conditional-probability-intermediate/</link><pubDate>Fri, 14 Nov 2025 09:00:00 +0200</pubDate><guid>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/lesson-2-conditional-probability-intermediate/</guid><description>Use the general multiplication rule to walk a probability tree, revisit independence through conditional probability, and learn the law of total probability — the denominator Bayes&amp;rsquo; theorem will need next.</description></item><item><title>Lesson 3 - Bayes' Theorem</title><link>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/lesson-3-bayes-theorem/</link><pubDate>Fri, 14 Nov 2025 09:00:00 +0200</pubDate><guid>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/lesson-3-bayes-theorem/</guid><description>Turn P(B|A) into P(A|B) with Bayes&amp;rsquo; theorem. Meet the prior, likelihood, evidence, and posterior, work the classic 16.7% medical-test result, and see why base rates change everything.</description></item><item><title>Lesson 4 - The Naive Bayes Algorithm</title><link>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/lesson-4-the-naive-bayes-algorithm/</link><pubDate>Fri, 14 Nov 2025 09:00:00 +0200</pubDate><guid>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/lesson-4-the-naive-bayes-algorithm/</guid><description>Build the Naive Bayes algorithm step by step: compare P(clickbait | words) with P(genuine | words), handle zero-frequency words with add-1 smoothing, use log-probabilities, and reach ~91% accuracy on real headlines.</description></item><item><title>Lesson 5 - Guided Project: Building a Clickbait Detector</title><link>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/lesson-5-guided-project-clickbait-classifier/</link><pubDate>Fri, 14 Nov 2025 09:00:00 +0200</pubDate><guid>https://datatweets.com/courses/statistics/conditional-probability-and-bayes/lesson-5-guided-project-clickbait-classifier/</guid><description>The module capstone: tokenize 6,000 real headlines, train a from-scratch Naive Bayes classifier with priors and smoothed word likelihoods, then measure a real ~91% test accuracy and read its confusion matrix and signal words.</description></item></channel></rss>