Machine Learning Course

Course Overview

This track is built for depth: over roughly 100 one-hour sessions we move through the ML lifecycle—data preparation, modeling choices, evaluation, and practical deployment concepts—always tied to exercises and projects you can explain in interviews. Pace and emphasis adjust to your background (school elective vs university vs job prep).

For extra reading alongside sessions, see articles on our blog (for example ML foundations, agents, and responsible AI topics) and the AI News index for library releases.

What You'll Learn:

• Foundations: Linear Algebra, Calculus, and Probability for ML.
• Supervised Learning: Linear/Logistic Regression, SVM, Decision Trees, Random Forests, Gradient Boosting.
• Unsupervised Learning: K-Means Clustering, PCA, and Dimensionality Reduction.
• Deep Learning Basics: Introduction to Neural Networks and frameworks like TensorFlow/PyTorch.
• Model Engineering: Feature Engineering, Hyperparameter Tuning, and Model Evaluation.
• Deployment: Learn to deploy your models as APIs for use in web applications.