Advanced Python and AI Programming
- Level High School
- Contact Hours 110
- Timeframe Year
The Advanced Python Programming course teaches students advanced programming concepts in Python and how to apply them to real-world problems. Students will implement advanced data structures and algorithms, apply object-oriented programming, tackle classical AI challenges, and explore a variety of Python libraries.
To view the entire syllabus, click here or click to explore the full course.
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Welcome to Advanced Python and AI
Students review and extend core Python skills, including program structure, documentation, formatted output, exception handling, and lambda functions, to prepare for the object-oriented and data-driven work ahead. |
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Object-Oriented Programming
Students learn to design and implement classes, using constructors, getters and setters, inheritance, polymorphism, and magic methods to model real-world entities in code. |
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Libraries and Packages
Students learn to use standard and third-party Python libraries to work with web data, perform numerical computing, clean and visualize datasets, and test their own code. |
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Build Your Own Adventure Game
Students plan and build an original text adventure game across a series of milestones, applying classes, packages, and program design skills from earlier modules to their own story. |
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Data Structures
Students learn to implement and apply core data structures, including sets, stacks, queues, linked lists, hash tables, trees, heaps, and graphs, to solve realistic problems. |
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Algorithms
Students analyze algorithm efficiency using Big O notation and implement classic search, sort, and recursive algorithms to understand their tradeoffs. |
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Build a Music Player
Students design and build a working music player across a series of milestones, applying custom data structures and search and sort algorithms to their own project. |
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Algorithms That Think
Students explore foundational artificial intelligence concepts, including graph search algorithms, heuristic and greedy search, and core machine learning models such as linear regression, k-nearest neighbors, decision trees, neural networks, and clustering. |
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Build a Classifier
Students design, build, train, and evaluate an original machine learning classifier across a series of milestones, culminating in a documented and presented final project. |
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Final Exam
Students complete a cumulative, multiple-choice exam that assesses their understanding of concepts from across the entire course, from Python fundamentals through object-oriented programming, data structures, algorithms, and AI/machine learning. |
Explore programs that your students will build throughout this course!
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