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Course Syllabus

2024/2025

Basic Python for Machine Learning

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type: Mago-Lego
Delivered by: International College of Economics and Finance
When: 3 module
Open to: students of one campus
Instructors: Madhwal Yash
Language: English
ECTS credits: 3

Course Syllabus

Full SyllabusAsk Question

Abstract

Basic Python for Machine Learning is an elective course for master’s-level students at ICEF, designed for participants with no prior programming experience. The course is structured in three progressive modules: it begins with the fundamentals of Python, building a clear understanding of syntax, logic, and core concepts through simple, real-world problems; it then moves into more advanced techniques, covering libraries, modular programming, and practical applications; finally, it connects programming with data science, introducing data visualization as a foundation. Using a solution-focused, hands-on approach, the course develops strong Python skills and transferable problem-solving abilities that support learning and applying any programming language.
Learning Objectives

Learning Objectives

  • Gain a thorough understanding of Python programming and its core concepts.
  • Strengthen problem-solving skills through a structured, programmable approach transferable to any programming language.
  • Learn to design and implement efficient, reusable code for real-world applications.
  • Develop proficiency in using Python libraries and modules to enhance functionality
  • Acquire foundational skills in data handling, visualization, and analysis as a stepping stone to machine learning.
  • Build confidence in approaching unfamiliar programming challenges with logical and systematic thinking.
Expected Learning Outcomes

Expected Learning Outcomes

  • Explaining python syntaxes, algorithm workflow, etc.
  • Debugging python codes.
  • Analyze the data sets and use python to visualize results.
  • Write Python programs effectively.
  • Apply logical and structured thinking to solve problems through programming.
  • Demonstrate confidence in approaching and solving problems in Python and other programming languages.
Course Contents

Course Contents

  • Introduction to Python
  • Advanced Python Programming
  • Applied Python in Machine Learning - 1
  • Applied Python in Machine Learning - 2
Assessment Elements

Assessment Elements

  • non-blocking Group Project
  • non-blocking Home assignments
  • non-blocking In-class assignment
Interim Assessment

Interim Assessment

  • 2024/2025 3rd module
    0.5 * Group Project + 0.3 * Home assignments + 0.2 * In-class assignment
Bibliography

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

Authors

  • Madval Iash