Applied Machine Learning Bootcamp Project
Timeline
-
June 9, 2022Experience start
-
June 10, 2022Project Client Discovery Session 6-8pm MT
-
June 17, 2022Team Formation 6-8pm MT
-
June 24, 2022Project Client Discovery Session 6-8pm MT
-
July 1, 2022Client Demos 6-8pm MT
-
July 8, 2022Client Demos 6-8pm MT
-
July 22, 2022Experience end
Timeline
-
June 9, 2022Experience start
-
June 10, 2022Project Client Discovery Session 6-8pm MT
Meeting between students and company to confirm project scope, communications, and deliverables.
-
June 17, 2022Team Formation 6-8pm MT
Project teams will be assigned to projects and clients will meet with their teams.
-
June 24, 2022Project Client Discovery Session 6-8pm MT
Meeting between students and company to confirm project scope, communications, and deliverables.
-
July 1, 2022Client Demos 6-8pm MT
In progress work will be demoed and feedback solicited.
-
July 8, 2022Client Demos 6-8pm MT
In progress work will be demoed and feedback solicited.
-
July 22, 2022Final Project Presentations 6-9pm MT
Students will present their final project deliverable to their clients.
-
July 22, 2022Experience end
Experience scope
Categories
Machine learning Artificial intelligenceSkills
machine learning data mining and analysis supervised and unsupervised learning algorithmsStudents 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
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, 2022Experience start
-
June 10, 2022Project Client Discovery Session 6-8pm MT
-
June 17, 2022Team Formation 6-8pm MT
-
June 24, 2022Project Client Discovery Session 6-8pm MT
-
July 1, 2022Client Demos 6-8pm MT
-
July 8, 2022Client Demos 6-8pm MT
-
July 22, 2022Experience end
Timeline
-
June 9, 2022Experience start
-
June 10, 2022Project Client Discovery Session 6-8pm MT
Meeting between students and company to confirm project scope, communications, and deliverables.
-
June 17, 2022Team Formation 6-8pm MT
Project teams will be assigned to projects and clients will meet with their teams.
-
June 24, 2022Project Client Discovery Session 6-8pm MT
Meeting between students and company to confirm project scope, communications, and deliverables.
-
July 1, 2022Client Demos 6-8pm MT
In progress work will be demoed and feedback solicited.
-
July 8, 2022Client Demos 6-8pm MT
In progress work will be demoed and feedback solicited.
-
July 22, 2022Final Project Presentations 6-9pm MT
Students will present their final project deliverable to their clients.
-
July 22, 2022Experience 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:
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.
Timeline
-
June 9, 2022Experience start
-
June 10, 2022Project Client Discovery Session 6-8pm MT
-
June 17, 2022Team Formation 6-8pm MT
-
June 24, 2022Project Client Discovery Session 6-8pm MT
-
July 1, 2022Client Demos 6-8pm MT
-
July 8, 2022Client Demos 6-8pm MT
-
July 22, 2022Experience end
Timeline
-
June 9, 2022Experience start
-
June 10, 2022Project Client Discovery Session 6-8pm MT
Meeting between students and company to confirm project scope, communications, and deliverables.
-
June 17, 2022Team Formation 6-8pm MT
Project teams will be assigned to projects and clients will meet with their teams.
-
June 24, 2022Project Client Discovery Session 6-8pm MT
Meeting between students and company to confirm project scope, communications, and deliverables.
-
July 1, 2022Client Demos 6-8pm MT
In progress work will be demoed and feedback solicited.
-
July 8, 2022Client Demos 6-8pm MT
In progress work will be demoed and feedback solicited.
-
July 22, 2022Final Project Presentations 6-9pm MT
Students will present their final project deliverable to their clients.
-
July 22, 2022Experience end