Machine Learning
Build predictive models. Supervised and unsupervised learning, model evaluation, and real-world applications.
Machine learning algorithms learn patterns from data to make predictions. This section covers supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), and practical considerations like model evaluation and selection. Applied finance ML courses are in the Finance Applications section.
What is Machine Learning?
Machine learning is about building systems that learn from data rather than being explicitly programmed. Instead of writing rules, you provide examples and the algorithm finds patterns. This is powerful for problems where rules are hard to specify, like predicting credit defaults or detecting fraud.
What You'll Learn
You'll learn the major algorithm families: regression for predicting continuous values, classification for categorizing observations, and clustering for finding natural groups. More importantly, you'll learn how to evaluate models properly, avoid overfitting, and select the right approach for different problems.
Machine Learning in Finance
Finance has embraced ML for credit scoring, algorithmic trading, fraud detection, and risk modeling. The foundational ML concepts are covered here. For applied finance projects using ML, see the Finance Applications section.
