Course Description
This course introduces students to the fundamentals of machine learning using the Python® programming language. Designed for beginners with little or no experience in machine learning, the course covers essential concepts such as supervised and unsupervised learning, model evaluation, and data preparation. Students will use real-world datasets and Python tools to build, train, and test their own machine learning models. Emphasis is placed on hands-on learning through coding exercises, projects, and visual explanations rather than advanced mathematics.
At the end of this course, students will be able to:
- Evaluate basic computer programs to solve problems using foundational programming concepts.
- Organize and analyze data to prepare it for further processing and interpretation.
- Evaluate simple predictive models to identify patterns and make informed decisions.
- Group data based on similarities to discover hidden structures or patterns.
- Evaluate model performance using appropriate methods to measure accuracy and reliability.
- Create visual representations of data and results to support clear communication and analysis.
- Interpret and explain model results in a way that is understandable to non-technical audiences.
- Recognize ethical considerations in data and AI, including fairness, transparency, and privacy.
General Education Distribution Area
AAS Business Elective
Topical Outline
- Learn the basics of Python®, including how to write simple programs and use common programming tools.
- Understand how to organize, clean, and work with data using Python® libraries.
- Explore how machines can “learn” from data by building simple models like decision trees and regression models.
- Discover how to find patterns in data.
- Learn how to check if their models are working well and how to spot common problems.
- Use tools and graphs to explore and explain data.
- Practice making sense of model results and using them to help answer real-world questions.
- Talk about the ethical side of machine learning, including fairness, privacy, and using AI responsibly.