Information Package / Course Catalogue
Introduction to Machine Learning With Python
Course Code: FEK530
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

This course aims to teach introduction to Python programming, Python basics, machine learning concepts, machine learning algorithms, data preprocessing and data visualization, and to make applications in Python.

Course Content

Throughout the semester, Python programming languages are explained with theoretical and practical examples and used in assignments. It is aimed to teach machine learning techniques.

Name of Lecturer(s)
Assoc. Prof. Elvan HAYAT
Learning Outcomes
1.Learns the basics of Python programming language.
2.Learn basic data analysis techniques in Python.
3.Masters the concepts of machine learning.
4.Learns about machine learning algorithms and makes applications with Python.
5.Learns about machine learning algorithms and makes applications with Python.
Recommended or Required Reading
1.Introduction to Machine Learning with Python: A Guide for Data Scientists, Andreas C. Müller and Sarah Guido (for beginners).
2.Python data science handbook : essential tools for working with data J. VanderPlas. O'Reilly Media, Inc, Sebastopol, CA, (2016)
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to Python Programming (Why Python?, Python and its application areas, Anaconda Navigator)
Week 2 - Theoretical & Practice
Python Fundamentals (Variables and Data Types, Basic operations, Comparison, Mathematical operators, Strings and string operations, Input / Output, Lists and list operations, Tuples, Dictionaries, Sets)
Week 3 - Theoretical & Practice
Conditions and Loops (if, elif, else, for, while, break, continue, pass)
Week 4 - Theoretical & Practice
Functions (Function definition and calling, Arguments, parameters, return values)
Week 5 - Theoretical & Practice
Machine Learning Fundamentals (Introduction to Machine Learning, Types of Machine Learning Algorithms: Supervised, Unsupervised and Reinforcement Learning)
Week 6 - Theoretical & Practice
Python for Machine Learning (NumPy, Pandas, Matplotlib, Seaborn)
Week 7 - Theoretical & Practice
Data Preprocessing (Data Cleaning, Missing Data Analysis, Outliers, Data transformation, Normalization, Encoding)
Week 8 - Intermediate Exam
Midterm
Week 9 - Theoretical & Practice
Introduction to Data Visualization (Matplotlib, Seaborn and Plotly modules, line, scatter, bar etc. graphs with Matplotlib, Element customizations: Label, Title, Axes, etc.)
Week 10 - Theoretical & Practice
Advanced Data Visualization (with Matplotlib and Seaborn)
Week 11 - Theoretical & Practice
Supervised Learning (Regression, Linear, Logistic, kNN, Decision Trees)
Week 12 - Theoretical & Practice
Supervised Learning (Evaluation of Model Performance, Cross-validation, Hyperparameter optimization)
Week 13 - Theoretical & Practice
Unsupervised Learning (Clustering, kMeans, DBSCAN, Hierarchical Clustering, Dimension Reduction, PCA)
Week 14 - Theoretical & Practice
Unsupervised Learning (Clustering, kMeans, DBSCAN, Hierarchical Clustering, Dimension Reduction, PCA)
Week 15 - Practice
Project Presentations
Week 16 - Final Exam
Final
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Individual Work73235
Midterm Examination1819
Final Examination18311
TOTAL WORKLOAD (hours)125
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
OÇ-1
5
5
5
5
4
4
4
4
4
OÇ-2
4
5
5
4
5
5
4
4
3
OÇ-3
4
5
5
5
4
4
4
4
5
OÇ-4
3
4
5
5
4
4
5
3
5
OÇ-5
4
4
5
4
5
3
5
4
4
Adnan Menderes University - Information Package / Course Catalogue
2026