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Azure Machine Learning (15 ECTS)

Code: TT00EO92-3006

General information


Timing
01.01.2023 - 31.12.2023
Implementation has ended.
Number of ECTS credits allocated
15 ECTS
Virtual portion
15 ECTS
Mode of delivery
Online
Campus
Karaportti 2
Teaching languages
English
Seats
0 - 1000
Degree programmes
Information and Communication Technology
Teachers
Virve Prami
Course
TT00EO92
No reservations found for implementation TT00EO92-3006!

Location and time

Course can be done with own pace in TechClass environment.

Materials

Lecture slides
Tutorial videos
Quizzes
Exercises
Project

Employer connections

N/A

Exam schedules

Online.

International connections

N/A

Completion alternatives

N/A

Evaluation methods and criteria

Exercises 50%
Quizzes 25%
Project 25%

Student workload

Lectures = 85h
Exercises = 95h
Self-study = 100h
Quizzes = 15h
Project = 65h
Total = 360 hours

Content scheduling

Up to Student her-/himself.

Teaching methods

100% online Self-Study course.

- Tutorial Videos
- Exercises
- Quiz
- Project
- Self-study

Evaluation scale

Hyväksytty/Hylätty

Assessment criteria, satisfactory (1)

- The student is familiar with the cloud-based service.
- The student is familiar with about Azure Machine Learning Studio.
- The student knows about how to use Azure Machine Learning Designer service.
- The student is familiar how to train a model using Azure Machine Learning Designer.
- The student is familiar with concept of Automated Machine Learning service in AML.
- The student is familiar with different compute resource in AML.

Assessment criteria, good (3)

- The student knows how to use Automated Machine Learning Service to implement and deploy different machine learning models.
- The student knows the how to use Jupyter notebook and run and manage it.
- The student is familiar with different assets in Azure Machine Learning Studio.
- The student is familiar with Azure Machine Learning SDK.
- The student knows how to setup his/her local computer.
- The student knows how to use Azure machine learning compute resource to train his/her models.
- The student is familiar with the concept of Experiment.
- The student is familiar with the concept of Event Grid.

Assessment criteria, excellent (5)

- The student knows how to create an environment for his/her model.
- The student can prepare his/her code for deploying different machine learning models.
- The student can understand the technical document of Azure Machine learning Services.
- The student is familiar with Microsoft cognitive service.
- The student knows how transfer learning models work.

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