Introduction to Machine Learning
Step into predictive modeling — train models that learn from data and deploy them to solve real problems.
Course overview
Students learn core algorithms that allow computers to learn from data, evaluate performance, and deploy models to solve classification and forecasting challenges.
Core curriculum
Four themed modules. Each module is a working block of lessons and labs.
ML Fundamentals
Core machine learning concepts, the data science pipeline, and essential Feature Engineering techniques.
Supervised Learning
Build and evaluate Linear Regression, Logistic Regression, and Decision Trees with real datasets.
Unsupervised Learning
Discover hidden groupings using K-Means clustering and dimensionality reduction via PCA.
Advanced Topics
Introduction to semi-supervised techniques, basic Reinforcement Learning, and final project reporting.
What you'll gain
- End-to-end ML pipeline experience: features → model → evaluation
- Hands-on practice with regression, classification, and clustering
- Ability to choose the right model for a real problem
- A final ML project with a written report
Ready to build with AI?
Schedule a free consultation. We'll help you choose the right track — Builder, Portfolio, Scholar, or Innovation — based on your goals and experience.