TEDA1036
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Introduction to Machine Learning
Data TechnologySchool of Technology and Service
Start Term
2026-2027
Course Title
Introduction to Machine Learning
Course Code
TEDA1036
Course Hours
60
Course Credits
2
Description
This course teaches the fundamental principles and practical applications of machine learning for data analysis. Students study essential topics including data preprocessing, exploratory data analysis, and the core concepts of supervised and unsupervised learning. Participants perform regression, classification, and clustering techniques using real-world data. This lays the groundwork to start building machine learning pipelines and approaching data science tasks.
Upon course completion, students will be able to:
• Produce datasets for machine learning through preprocessing data techniques in Python such as handling missing data, outlier treatment and encoding categorical variables.
• Distinguish between supervised and unsupervised learning techniques, like regression, classification, and clustering, using Python.
• Construct machine learning models using Python libraries with real-world datasets.
• Evaluate machine learning models using various validation techniques in Python to gauge their performance and potential limitations.
• Execute end-to-end machine learning projects in Python, encompassing data preprocessing, model construction, evaluation, and drawing insights.
