Course Catalog
Machine Learning Foundations with Python
90-803
Units: 12
Description
This course equips students with the skills to build, evaluate and deploy machine learning models using Python. Emphasis is placed on feature engineering, supervised and unsupervised learning, model selection and common data handling and analysis practices. Students will complete quizzes, a capstone project using real-world datasets and a take a final exam.
Learning Outcomes
- Learn the fundamentals of machine learning and required tools and skills using python.
- Be able to produce, comprehend and run Python code for commonly used machine learning methods.
- Understand feature engineering concepts, and be able to place them into practice through different types of data.
- Understand and apply both supervised and unsupervised learning techniques
- Understand the advantages and disadvantages of machine learning algorithms. This involves generalizability, bias-variance trade-off, and interpretability-flexibility tradeoff.
- Be able to choose appropriate model/s for a dataset and evaluate the performance and reliability of such model/s.
- Be able to apply machine learning to real-world data.
- Understand and follow equitable and good data practices.
Prerequisites Description
(90819 or 95888) and (90707 or 90711 or 95796) and (90800)