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Aug 05, 2026
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2026-2027 Yavapai College Catalog
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AIM 205 - Introduction to Machine Learning
Description: Introduction to machine learning concepts and Python applications, including data acquisition, supervised and unsupervised learning, and data modeling. Explores how Deep Learning extends the field of Machine Learning, applying key metrics such as accuracy, precision, and recall to evaluate model performance. Emphasis is placed on developing Python-based projects and visual dashboards using Tableau to interpret and communicate results. Practical applications of neural networks and exploring emerging trends shaping the future of machine learning.
Prerequisites: CSC 105 and CSC 113
Credits: 3
Lecture: 2
Lab: 2
Course Content:
- Introduction to Machine Learning
- Mathematical and programming foundations
- Data acquisition and preparation
- Supervised and Unsupervised learning models
- Reinforcement learning and model evaluation
- Neural Networks and Deep Learning
- Data visualization and communication
- Ethics, trends, and future of Machine Learning
Learning Outcomes:
- Describe Machine Learning and how Deep Learning is a subset of the wider field of Machine Learning. (1, 6, 8)
- Interpret the foundational tools required for building Machine Learning projects. (2, 3)
- Develop a simple dashboard for visualizing data. (3, 7)
- Compare models used in Supervised, Unsupervised, and Reinforcement Learning. (4, 5, 6)
- Describe common terms and concepts used across the AI project cycle. (3, 4, 5, 6)
- Explain the working principles of Neural Networks and how they relate to biological neurons. (1, 6, 8)
- Develop Python-based Machine Learning use cases and projects using appropriate methodologies. (2, 4, 5, 6, 7)
- Discuss the future of Machine Learning based on current and emerging trends. (6, 8)
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