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Applied machine learning

Individual course

Min amount of FITech students: 4
Max amount of FITech students: 30 (including max. 5 degree students)

This course provides an insight into the implementation of machine learning for applications. The course covers methods needed for data analysis from reading and cleaning the data, imputation of missing values, extracting features and application of machine learning methods to develop an optimized pipeline leading from data to knowledge. The course introduces many machine learning algorithms and discusses their advantages and limitations. Methods for data and model visualization and reporting are utilized throughout the course.

Course contents

  • Introduction to machine learning
  • Introducing Python
  • Reading and cleaning data and plotting
  • Preprocessing and feature extraction
  • Unsupervised ML for data exploration
  • Supervised machine learning
  • Evaluation and optimisation of the models

Learning outcomes

Students who complete this course successfully will be aware of the practical implementation and usage of machine learning algorithms. Furthermore, they will be able to apply machine learning algorithms in real problems using efficient programming languages, for example Python.

Course material

VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data (1 edition).

Completion methods

The course evaluation is based on quizzes, weekly exercises and a machine learning project.

You can get a digital badge after completing this course.

 

Responsible teacher

University of Vaasa
Petri Välisuo
petri.valisuo(at)uwasa.fi

Contact person for applications

FITech Network University
Monica Sandberg
monica.sandberg(at)fitech.io
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Category:
ICT Studies
Topic:
5G technology
Course code:
ICAT3210
Credits:
5 ECTS
Price:
0 €
Level:
Teaching period:
27.9–26.11.2021
Application deadline:
Application period has ended
Host university:
University of Vaasa
Study is open for:
Adult learner,
Degree student
Teaching methods:
Online
Language:
English
General prerequisites:
Some programming experience and basic knowledge in statistics are highly recommended prerequisites. Previous Python programming experience is not required.
Study suitable for:
The course is suitable for students who have experience in programming, and want to learn machine learning and data science or learn how apply machine learning efficiently with Python.
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