Machine Learning 2

DAB300
Closed
Main contact
St. Clair College
Windsor, Ontario, Canada
Professor
(1)
2
Timeline
  • September 21, 2020
    Experience start
  • August 15, 2020
    Project Scope Meeting
  • August 22, 2020
    Progress Report
  • September 8, 2020
    Final Presentation
  • December 12, 2020
    Experience end
Experience
5 projects wanted
Dates set by experience
Preferred companies
Anywhere
Any
Any industries

Experience scope

Categories
Information technology Data analysis
Skills
business analytics data analysis research data analysis, data science concepts, text analytics model development deployment and documentation
Learner goals and capabilities

This is the second of two machine learning courses that students in our Data Analytics for Business graduate certificate program are required to take. Students gain skills in data representation, feature selection, model evaluation and improvement, as well using fully-connected and convolutional neural networks. This is a project based course where the students focus on building and applying statistical and predictive models in solving practical problems. All work is done in Python, although students will have varying familiarity with R and SQL from other coursework.

Learners

Learners
Graduate
Any level
230 learners
Project
120 hours per learner
Learners self-assign
Teams of 5
Expected outcomes and deliverables

The project will provide:

  • A final report detailing the problem, attempted solutions, and results, including benchmarks, if applicable.
  • Ideas for next steps in the process based on project outcomes.
Project timeline
  • September 21, 2020
    Experience start
  • August 15, 2020
    Project Scope Meeting
  • August 22, 2020
    Progress Report
  • September 8, 2020
    Final Presentation
  • December 12, 2020
    Experience end

Project examples

The course provides an opportunity for businesses and learners to collaborate to identify and translate a real business problem into a machine learning problem. The project can be an end-to-end machine learning application, including data preparation, model creation and evaluation and potentially methods for deployment. Project results/ recommendations will be communicated in a final report.

You should submit a high-level proposal/business problem statement including a clear connection to machine learning, relevant data sets and definitions, a list of acceptable tools (if applicable), and expected deliverables. Business datasets could be provided based on a non-disclosure agreement or in an anonymized/synthetic data format that is relevant to your organization and business problem. The course instructor will review the documents to confirm the scope and timing of the proposed problem and its alignment with the course requirements.

Additional company criteria

Companies must answer the following questions to submit a match request to this experience:

  • Q1 - Checkbox
  • Q2 - Checkbox
  • Q3 - Checkbox
  • Q4 - Checkbox
  • Q5 - Checkbox