This training is aimed at professional development and the certificate obtained does not replace the MYK authorization certificate required in MYK authorized trainings.
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
Prerequisite: Completion of preparatory courses (Programming / OS / Networking) is required; basic mathematics (especially statistics, probability) knowledge is recommended.
Beginner Level (Day 1-5)
Day 1: Fundamentals and Data Preparation
• Concepts of Artificial Intelligence and Machine Learning; types
• Basic statistics and probability: mean, variance, distributions, hypothesis testing
• Data collection / dataset structure, data cleaning, converting categorical vs numerical data
Day 2: Exploratory Data Analysis & Visualization
• Data visualization techniques: understanding graphs, drawing inferences
• Feature engineering: feature selection, creating new features
Day 3: Basic Algorithms and Model Evaluation
• Algorithms like linear regression, logistic regression, decision trees, k-NN
• Performance evaluation metrics: accuracy, precision, recall, F1, ROC AUC
Day 4: Advanced Basic Techniques + Simple NLP / Time Series
• Naive Bayes, SVM, clustering techniques
• Time series basics
• Basic natural language processing: text preprocessing, word vectors
Day 5: Mini Project and Portfolio Work
• Participants select a field, choose a dataset, and build a model
• Model creation, evaluation, presentation
Intermediate Technical Level (Day 6-10)
Day 6: Introduction to Deep Learning
• Artificial neural networks, layers, activation functions, forward-backward propagation
• CNN: basic image processing applications
Day 7: Advanced Models & NLP
• Transformer architectures, language models, BERT / GPT-type concepts (with examples, at a conceptual level)
• Time series models, anomaly detection
Day 8: Model Optimization and Deployment
• Hyperparameter tuning, overfitting/underfitting, regularization
• Deploying a simple model: exposing it as an API / using it as a web service
Day 9: Prompt Engineering and Techniques
• Few-shot / zero-shot / chain-of-thought prompt techniques
• Iterative prompt refinement, output calibration
• Industry-specific prompt templates
Day 10: Ethics, Compliance, and Career Applications + Closing Project
• KVKK, GDPR, ethical use, data privacy
• Responsibility, bias, fairness, transparency in AI systems
• Participant project presentations + evaluation
Training Outcomes
- Participants will learn basic and intermediate machine learning techniques.
- They will gain practical experience on real datasets.
- Participants will develop skills to start, manage, and report on AI projects.
- They will become aware of ethical and legal frameworks.
Training Notes
- The trainer may adapt the content based on the level of the participant group.
- The program is independent of technology and focuses on general principles.
- At the end of the training, participants will receive a university-approved certificate.
This training is open for corporate collaboration (packages for institutional/company legal entities), and individual applications are not accepted. The training content can be restructured based on the corporate participant profile and needs. After mutual discussions, the scope and method of the training (In-person, Online) will be determined, and the relevant processes will be completed. If an agreement is reached, the suitable dates and times for your institution’s participants and the location of the training will be set.