Duration
Annual
Prerequisites
None required. If you are taking this course as part of the Artificial Intelligence Foundations Program of Study, Artificial Intelligence in the World, Applications of Artificial Intelligence, and Procedural Programming should be taken first.
Requirements
None
Course Summary
In this course, you will deepen your understanding of machine learning. You will examine how and why the concept of machine learning was developed. Excel and Python will be used to analyze data and training models. Finally, you will discover what the future of machine learning looks like and the importance of the development cycle. Foundations of Machine Learning is the fourth course in the Artificial Intelligence (AI) Foundations program of study in the Engineering Technology cluster.
Segment One:
• Machine learning vs. human learning
• Abstraction
• Types of representations
• Data structures
• Search algorithms
• AI vendors
• Supervised, unsupervised, and reinforcement learning
• Learning algorithms
• Classification
• Neural networks
• Training models with data
• Personal, geospatial, time-based data, and company
Segment Two:
• Problem-solving and data
• APIs, RSSs, and web scraping
• SQL and NoSQL databases
• Data wrangling
• Statistical sampling and testing
• Identifying patterns in data
• Data analysis techniques
• ML model building
• Errors in decisions and predictions
• Privacy and security concerns with data
• ML development process
• Adjust and evaluate the model
• Ethical problems related to ML
• GPUs and CPUs
• Fairness in AI
• Privacy and security concerns
In Foundations of Machine Learning, students build subject knowledge through structured online lessons, guided practice, and regular feedback from qualified teachers. The course supports flexible study while maintaining clear academic expectations and steady progress.
Students can revisit learning materials, complete assignments at a manageable pace, and use teacher guidance to strengthen understanding. Families can follow progress while learners develop organization, independent study habits, and confidence.