Units
3.0 QUARTER UNITS

Course Description


This course introduces students to the Python programming language essential for data manipulation, statistical analysis, and predictive modeling techniques required for machine learning and artificial intelligence.

 

We will explore the wonderfully concise and expressive use of Python's advanced module features and apply it in probability, statistical analysis, training models, and various other applications. Students will explore mathematical operations with array data structures, optimization, probability density function, interpolation, visualization, and other high-performance benefits of core scientific packages such as NumPy, Pandas, scikit-learn, and Matplotlib.

Additionally, students will learn modern machine learning concepts and techniques, including supervised, unsupervised, and semi-supervised learning, to develop predictive models using Python libraries. The course concludes with a real-world, end-to-end machine learning project, providing students with practical experience in solving challenging problems.


 

Topics

  • Training models
  • Random forests
  • Dimensionality reduction
  • Clustering methods

Prerequisites / Skills Needed

 

Basic Programming Knowledge as can be acquired in Python Programming for Beginners (CMPR.X415) and a knowledge of Fundamentals of Statistics

 

Additional Information

AI* - This course uses Generative AI through hands-on labs, to develop the skills needed to implement and evaluate ML models effectively. 

Syllabus Library
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This course applies to these programs:

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