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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
  • October 18, 2021
    Experience start
  • June 15, 2021
    Project Scope Meeting
  • October 22, 2021
    Client Discovery Session 1
  • October 26, 2021
    Client Demo Session 2
  • October 29, 2021
    Client Discovery Session 2
  • November 19, 2021
    Client Demo Session 1
  • December 11, 2021
    Experience end
Experience
1/4 project matches
Dates set by experience
Preferred companies
Alberta, Canada
Any
Any industries

Experience scope

Categories
Data analysis
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

Learners
Bootcamp
Any level
24 learners
Project
78 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
  • October 18, 2021
    Experience start
  • June 15, 2021
    Project Scope Meeting
  • October 22, 2021
    Client Discovery Session 1
  • October 26, 2021
    Client Demo Session 2
  • October 29, 2021
    Client Discovery Session 2
  • November 19, 2021
    Client Demo Session 1
  • December 11, 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:

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

Attend client meetings on the evenings of Oct 21st, Oct 28th, Nov 18th , Nov 25th and Dec 9th.