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Applied Machine Learning Bootcamp

DIGI 004
Closed
SAIT
SAIT
Calgary, Alberta, Canada
Bradley Ackroyd
Project Coordinator, School for Advanced Digital Technology
(2)
3
Timeline
  • June 14, 2021
    Experience start
  • June 15, 2021
    Project Scope Meeting
  • July 21, 2021
    Experience end
Experience
1/13 project matches
Dates set by experience
Preferred companies
Anywhere
Any
Any industries

Experience scope

Categories
Data analysis
Skills
data mining and analysis supervised and unsupervised learning algorithms machine learning
Learner goals and capabilities

The Southern Alberta Institute of Technology and Braintoy are partnering in the delivery of a 12 week Applied Machine Learning Bootcamp. Our students engage in an individual final machine learning project that spans 3 weeks. This project culminates in the development of a machine learning model that predicts, detects, or forecasts an entity. The data for the use case could be images (computer vision), text (natural language processing), time series (multi-variate or univariate), or tablular data. The data format would be a folder of images or comma-separated values (CSVs) for text, time series, or tablular data. The client will need to:

1) Provide a clearly defined machine learning problem.

2) Explain how the client intends to use the solution.

3) Explain why this problem needs to be solved.

4) Provide a subject matter expert that can be a touch point for the student and answer questions related to the data and use case.

Learners

Learners
Bootcamp
Any level
13 learners
Project
60 hours per learner
Learners self-assign
Individual projects
Expected outcomes and deliverables

Students will produce a proof of concept, predictive machine learning model (i.e. a minimally viable product) that solves a client problem.

Project timeline
  • June 14, 2021
    Experience start
  • June 15, 2021
    Project Scope Meeting
  • July 21, 2021
    Experience end

Project Examples

Requirements

Examples of student-developed predictive machine learning models:

  • Electricity consumption predictions or electricity load forecasting.
  • Facial recognition.
  • Solar power generation prediction.
  • Oil production prediction.
  • Carbon emission prediction.
  • Heart attack prediction.
  • Credit fraud detection.
  • Predicting customers who are a potential flight risk (customer churn).
  • Using MRI images to detect and predict patients who may have brain tumor.
  • Using chest ray images of patients to predict patients who are at risk of getting covid.

Additional company criteria

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

Be available for a quick phone call with the instructor to initiate your relationship and confirm your scope is an appropriate fit for the course.

Provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.