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Applied Machine Learning Bootcamp Project

PROJ 011
Closed
SAIT
SAIT
Calgary, Alberta, Canada
BA
Bradley Ackroyd
Project Coordinator, School for Advanced Digital Technology
(2)
3
Timeline
  • June 9, 2022
    Experience start
  • June 10, 2022
    Project Client Discovery Session 6-8pm MT
  • June 17, 2022
    Team Formation 6-8pm MT
  • June 24, 2022
    Project Client Discovery Session 6-8pm MT
  • July 1, 2022
    Client Demos 6-8pm MT
  • July 8, 2022
    Client Demos 6-8pm MT
  • July 22, 2022
    Experience end
Experience
6 projects wanted
Dates set by experience
Preferred companies
Alberta, Canada
Any
Any industries
Categories
Machine learning Artificial intelligence
Skills
machine learning data mining and analysis supervised and unsupervised learning algorithms
Learner goals and capabilities

Students from the SAIT's Applied Machine Learning Bootcamp and our Applied Product Management Bootcamp participate in a 78 hour interdisciplinary machine learning capstone project. 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
Bootcamp
Any level
24 learners
Project
80 hours per learner
Learners self-assign
Teams of 4
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 9, 2022
    Experience start
  • June 10, 2022
    Project Client Discovery Session 6-8pm MT
  • June 17, 2022
    Team Formation 6-8pm MT
  • June 24, 2022
    Project Client Discovery Session 6-8pm MT
  • July 1, 2022
    Client Demos 6-8pm MT
  • July 8, 2022
    Client Demos 6-8pm MT
  • July 22, 2022
    Experience end
Project Examples

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

A representative of the company will be available to attend weekly sprint meetings on the evenings of June 9th, 16th, 23rd, 30th, July 7th and 14th. A representative of the company will also be available to attend a final project presentation on the evening of July 21st.

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