Probability for Machine Learning

Overview

What you'll learn

• Build confidence with foundational probability concepts—terminology, sum & multiplication rules, and the Law of Large Numbers.
• Explore Bayes’ Theorem in action, from conditional probabilities to classification models that adapt as you gain new information.
• Understand and apply probability distributions—PMFs, PDFs, expected value, binomial vs. multinomial—to model discrete and continuous variables.
• Evaluate and compare models using ROC and precision‑recall curves—learn when each is most effective (e.g., imbalanced classes, critical false positives).

Skills you'll practise

Data ModelingMathematicsProbabilityCuriosityForecastingAlgebraData SciencePerformance MetricData-Driven Decision MakingUncertainty ModelingMachine LearningArtificial IntelligenceAlgorithmsProbability DistributionMachine Learning AlgorithmsIntelligent SystemsSoftware EngineeringBayesian StatisticsAdvanced Mathematics

Advantages and disadvantages

Some points apply to every course of this kind; see how we rank.

Taught by

• Emiko SanoPh.D. Educator and Data Scientist