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Course Description

This course will equip you with the basic machine learning and artificial intelligence (AI) tools for mining and analyzing datasets, and extracting insights for decision making. You will learn how to identify correlations and patterns in datasets, build predictive models using machine learning and AI software and evaluate the performance of those models.

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Learning Outcomes

  • Find correlations between variables in a dataset.
  • Identify clusters in data such as market segments.
  • Predict future outcomes based on hidden relationships in historical data.
  • Use tools to classify events or observations by type.
  • Evaluate and combine models for best performance.

Notes

This course is offered in partnership with University of Waterloo - WatSPEED and hosted on their learning platform.
System Requirements: Anaconda/Jupyter (software that you are required to install); Waterloo LEARN

Prerequisites

3250 - Foundations of Data Science or 3583 - Foundations of Data Science - ONLINE or a passing grade on a self-assessment test/quiz for equivalent skills
AND
3251 - Statistics for Data Science or 3584 - Statistics for Data Science - ONLINE or Previous knowledge and experience in Python programming and Statistical techniques, OR A passing grade on Pre-Requisite Self-Assessment  test/quiz for equivalent skills.

Recommendations

A degree in Engineering, Mathematics, or Computer Science is recommended, but not required. Basic knowledge of programming and programming languages is strongly recommended.

This course may be applied towards the SCS Certificate(s) in

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