Georgia Institute of Technology: Machine Learning Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course offers a comprehensive introduction to machine learning, designed for learners with some background in programming and mathematics. Over approximately 15–20 hours of content, you'll progress through six structured modules that build from foundational data exploration to advanced modeling techniques. The curriculum emphasizes hands-on practice, real-world applications, and industry-standard tools, preparing you for practical implementation in data science and AI roles.
Module 1: Data Exploration & Preprocessing
Estimated time: 2 hours
- Introduction to key concepts in data exploration & preprocessing
- Hands-on exercises applying data exploration & preprocessing techniques
- Review of tools and frameworks commonly used in practice
Module 2: Statistical Analysis & Probability
Estimated time: 4 hours
- Discussion of best practices and industry standards in statistical analysis
- Application of probability methods to real-world datasets
- Case study analysis with real-world examples
- Guided project work with instructor feedback
Module 3: Machine Learning Fundamentals
Estimated time: 2 hours
- Introduction to key concepts in machine learning fundamentals
- Interactive lab: Building practical solutions
- Case study analysis with real-world examples
Module 4: Model Evaluation & Optimization
Estimated time: 4 hours
- Hands-on exercises applying model evaluation & optimization techniques
- Assessment of model performance using real-world metrics
- Guided project work with instructor feedback
Module 5: Data Visualization & Storytelling
Estimated time: 3 hours
- Review of tools and frameworks for effective data visualization
- Techniques for communicating insights through storytelling
- Guided project work with instructor feedback
Module 6: Advanced Analytics & Feature Engineering
Estimated time: 3 hours
- Introduction to key concepts in advanced analytics & feature engineering
- Hands-on exercises applying advanced analytics & feature engineering techniques
- Review of tools and frameworks commonly used in practice
- Guided project work with instructor feedback
Prerequisites
- Basic proficiency in Python programming
- Familiarity with introductory statistics and linear algebra
- Some prior experience with data analysis or coding recommended
What You'll Be Able to Do After
- Build and evaluate machine learning models using real-world datasets
- Master exploratory data analysis workflows and best practices
- Apply statistical methods to extract insights from complex data
- Design end-to-end data science pipelines for production environments
- Work with large-scale datasets using industry-standard tools