By scipio on Skatehive
Learn AI Series (#32) - Bayesian Methods - Thinking in Probabilities What will I learn You will learn Bayesian vs frequentist thinking -- two fundamentally different views of what probability even means; Bayes' theorem applied to real ML problems, step by step from scratch; Naive Bayes classifier -- simple, fast, and surprisingly effective for text classification; building Bayesian inference from scratch so you see exactly how priors update to posteriors; Bayesian optimization for hyperparameter tuning -- smarter than grid search; when Bayesian approaches outperform point estimates, and when they're overkill; the connection between Bayesian priors and the regularization we already know from episode #11. Requirements A working modern computer running macOS, Windows or Ubuntu; An installed Python 3(.11+) distribution; The ambition to learn AI and machine learning. Difficulty Beginner Curriculum (of the Learn AI Series): Learn AI Series (#1) - What Machine Learning Actually Is Learn AI Se