Machine Learning
Timeline
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September 1, 2019Experience start
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October 10, 2019something
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February 11, 2020Project Scope Meeting
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February 29, 20202nd Touch Point
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April 4, 2020Project Presentation
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December 3, 2019Experience end
Timeline
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September 1, 2019Experience start
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October 10, 2019something
something else
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February 11, 2020Project Scope Meeting
Meeting between students and company to confirm: project scope, communication styles, and important dates.
-
February 29, 20202nd Touch Point
Evaluating initial findings, clarifying possible doubts, and revisiting plan for the remainder of the project.
-
April 4, 2020Project Presentation
Presenting the project to the company
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December 3, 2019Experience end
Categories
Data analysis Sales strategy Marketing strategySkills
business analytics business consulting data analysisThroughout the course students under instructor supervision will create machine learning solutions to enable your organization to predict future events of interest.
The final project deliverables will include:
- The software created to solve the machine learning problem
- Recommendation report on how to use the model
- A final presentation
Project timeline
-
September 1, 2019Experience start
-
October 10, 2019something
-
February 11, 2020Project Scope Meeting
-
February 29, 20202nd Touch Point
-
April 4, 2020Project Presentation
-
December 3, 2019Experience end
Timeline
-
September 1, 2019Experience start
-
October 10, 2019something
something else
-
February 11, 2020Project Scope Meeting
Meeting between students and company to confirm: project scope, communication styles, and important dates.
-
February 29, 20202nd Touch Point
Evaluating initial findings, clarifying possible doubts, and revisiting plan for the remainder of the project.
-
April 4, 2020Project Presentation
Presenting the project to the company
-
December 3, 2019Experience end
Project Examples
Machine learning is a field in computer science that uses statistical techniques to give computer systems the ability to learn from data without being explicitly programmed. Developing effective machine learning is often challenging; finding patterns is difficult and there is often not enough training data available.
Project examples include, but are not limited to:
- Predicting future events of interest: For instance, in marketing, machine learning enables businesses to predict customer intents, including purchasing a product or terminating their service contract. In area of predictive maintenance, machine learning can predict health status of different equipments and help business take proactive action towards maintaining operating at low cost.
- Spotting anomalies in data sets: Detecting fraud is a use case in point of sales and banking transactions
Companies must answer the following questions to submit a match request to this experience:
Provide an opportunity to students to present their work and receive feedback
Be available for at least 2 follow up meetings (phone call, preferably in-person) with the students to monitor the progress, clarify doubts, and answer questions.
Provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.
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.
Timeline
-
September 1, 2019Experience start
-
October 10, 2019something
-
February 11, 2020Project Scope Meeting
-
February 29, 20202nd Touch Point
-
April 4, 2020Project Presentation
-
December 3, 2019Experience end
Timeline
-
September 1, 2019Experience start
-
October 10, 2019something
something else
-
February 11, 2020Project Scope Meeting
Meeting between students and company to confirm: project scope, communication styles, and important dates.
-
February 29, 20202nd Touch Point
Evaluating initial findings, clarifying possible doubts, and revisiting plan for the remainder of the project.
-
April 4, 2020Project Presentation
Presenting the project to the company
-
December 3, 2019Experience end