Probability, Statistics & Calculus for AI

<p>Follow Aanya Verma, a junior ML engineer at the fictional startup Loopline, as she debugs and rebuilds the spam-filtering brain of Loopline's product, Sift. Along the way you'll learn the exact math tools she needs to solve each real problem: probability distributions and random variables (why a classifier's confidence score was meaningless), Bayes' theorem (building a transparent Bayesian spam filter from word frequencies), expectation and variance (setting a decision threshold based on the real cost of mistakes, and explaining noisy daily metrics), and derivatives, gradients, and the chain rule (debugging a neural network that refused to train, and understanding backpropagation from first principles). Every concept is introduced through a worked numeric example and small Python snippets, never as lecture-then-practice — this is beginner-friendly and assumes no prior calculus or statistics coursework.</p>

Tools: Probability Theory, Bayes' Theorem, Statistics, Derivatives & Gradients, Gradient Descent Math