Conditional Probability and Naive Bayes in Python

Understanding how events influence one another is the key to making accurate predictions in data science and machine learning. This course demystifies the mathematics of probability and shows you how to translate these concepts directly into clean, modern Python code. You will transition from understanding basic probability to implementing complex conditional rules and predictive algorithms. By studying clear written explanations and analyzing well-structured code snippets, you will build a solid theoretical foundation and apply it to solve real-world classification problems. What you'll learn: - Understand foundational probability concepts, including theoretical, empirical, and joint probabilities. - Apply core counting techniques such as permutations, combinations, and the product rule using Python. - Calculate conditional probabilities and evaluate statistical independence between different events. - Implement Bayes's theorem to systematically update probabilities based on prior knowledge. - Build a functional SMS spam classifier from scratch using the multinomial Naive Bayes algorithm. - Write clean, readable probability calculations using modern Python type hints and standard libraries. The course begins with core terminology and foundational math rules before guiding you through conditional logic and statistical independence. You will then explore Bayes's theorem and walk through the step-by-step logic of creating a text classification model. This course is designed for beginners who have a basic familiarity with Python syntax; no advanced mathematical background is required. Start reading today to unlock the predictive power of probability in Python.