Training Content
Training Duration: 10 Days (09:00–12:00 Theory, 13:00–16:00 Practice)
Level: Beginner + Intermediate Technical
Participant Profile: Software developers, data analysts, engineers, university students, corporate employees
Prerequisites: Preparatory courses (Programming / OS / Networking) must be completed; basic math knowledge (especially statistics and probability) is recommended.
Beginner Level (Days 1–5)
Day 1: Fundamentals and Data Preparation
• Concepts of artificial intelligence and machine learning; types of ML
• Basic statistics and probability: mean, variance, distributions, hypothesis tests
• Data collection / dataset structure, data cleaning, converting categorical vs numerical data
Day 2: Exploratory Data Analysis & Visualization
• Data visualization techniques: understanding graphs, making interpretations
• Feature engineering: feature selection, creating new features
Day 3: Basic Algorithms and Model Evaluation
• Algorithms such as linear regression, logistic regression, decision trees, k-NN
• Performance evaluation metrics: accuracy, precision, recall, F1, ROC AUC
Day 4: Advanced Basic Techniques + Basic NLP / Time Series
• Naive Bayes, SVM, clustering techniques
• Basics of time series
• Basic natural language processing: text preprocessing, word vectors
Day 5: Mini Project and Portfolio Study
• Dataset selection and model building for chosen domain
• Model building, evaluation, presentation
Intermediate Technical Level (Days 6–10)
Day 6: Introduction to Deep Learning
• Artificial neural networks, layers, activation functions, forward–backpropagation
• CNN: basic image processing applications
Day 7: Advanced Models & NLP
• Transformer architectures, language models, BERT / GPT concepts
• Time series models, anomaly detection
Day 8: Model Optimization and Deployment
• Hyperparameter tuning, overfitting/underfitting, regularization
• Basic model deployment: exposing a model as API / web service
Day 9: Prompt Techniques and Prompt Engineering
• Few-shot / zero-shot / chain-of-thought prompting
• Iterative prompt refinement, output calibration
• Profession/sector-specific prompt templates
Day 10: Ethics, Compliance, Career Applications + Final Project
• KVKK, GDPR, ethical usage, data privacy
• Responsibility in AI systems, bias, fairness, transparency
• Participant project presentations + evaluation
Training Outcomes
• Participants learn beginner and intermediate machine learning techniques.
• Gain hands-on experience with real datasets.
• Acquire skills to initiate, manage, and report AI projects.
• Develop awareness of ethical and legal frameworks.
Training Notes
• Instructors may adapt the content depending on participant level.
• The program is technology-independent and focuses on core principles.
• Participants receive a university-approved certificate upon completion.