2024/2025
Basic Python for Machine Learning
Type:
Mago-Lego
Delivered by:
International College of Economics and Finance
When:
3 module
Open to:
students of one campus
Language:
English
ECTS credits:
3
Contact hours:
32
Course Syllabus
Abstract
Basic Python for Machine Learning is an elective course for the master level students at ICEF. The course runs in the 3 module. The course consists of three parts. In the first part we cover the basic understanding of python language, how it work and solve some use cases using those python programming. The second part of the course will be little advanced where we will look advance programming, use of libraries, modules. The last part of the course introduces will be a prerequisite of the machine learning understanding, looking into data visualization, analysis, prediction, etc. We use a solution based approach using programming and solve task. Prerequisites: none, but familiarity with basic programming concepts is recommended.
Learning Objectives
- The course offers a thorough understanding of the workings and python programming.
- The course offers a thorough understanding of the workings and python programming.
Expected Learning Outcomes
- Explaining python syntaxes, algorithm workflow, etc.
- Debugging python codes.
- Analyze the data sets and use python to visualize results.
Course Contents
- Introduction to Python
- Advanced Python Programming
- Applied python in Machine Learning - 1
- Applied python in Machine Learning - 2
Interim Assessment
- 2024/2025 3rd module0.5 * Group Project + 0.3 * Home assignments + 0.2 * In-class assignment
Bibliography
Recommended Core Bibliography
- Eric Matthes. (2019). Python Crash Course, 2nd Edition : A Hands-On, Project-Based Introduction to Programming: Vol. 2nd edition. No Starch Press.
Recommended Additional Bibliography
- Guido Van Rossum, & Fred L. Drake. (2004). Python/C API Reference Manual Release 2.3.4. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.2FEE239A