Explore the full spectrum of AI: generative AI, machine learning, digital creation, software, agents, law and industry applications. Build a training path that fits your goals from 60 modules across 12 subject areas.
For planning purposes, a training day is based on 5 hours of learning, excluding breaks and lunch. Example schedule: 09:30–16:00, with 90 minutes of breaks in total. Daily duration and times are agreed together to suit your organization’s needs and calendar.
The full spectrum of artificial intelligence
Choose a subject area to explore its modules. Beyond the ready-made programs, the catalog includes standalone courses and in-depth labs.
Ready-made training paths
1 day for an overview, 2 days for generative AI practice, or 5 days for a broad AI program. Each path has its own daily schedule and intended audience.
1 day5 hours · net training time
An Overview of Artificial Intelligence
Get to know the main fields of AI, generative models and the tool ecosystem. Try prompting, establish safe usage practices and define your personal learning path.
Who is it for?
Professionals, managers, educators and beginner teams who want to understand the world of AI.
Prerequisites
Basic computer skills. No coding knowledge required.
Target outcome
A map of AI fields, a tested prompt, a safe-use checklist and a personal learning plan.
Explore the daily schedule
Day 1 · The world of AI, tools and a safe start
5 hours · net training time
AI literacy and its evolution30 min · Concepts and discussion
AI fields and core concepts30 min · Concepts and discussion
How do large language models work?30 min · Demonstration and review
Foundational prompt engineering workshop1 hour · Guided practice
The AI tool ecosystem and multimodal use45 min · Demonstration and review
Safe use, source checks and output review45 min · Guided practice
How AI changes work and use cases30 min · Concepts and discussion
Your personal practice and learning path30 min · Guided practice
Tool options
A topic map, whiteboard or Miro; printed templates as an alternative
An approved assistant: ChatGPT, Claude, Gemini or Microsoft 365 Copilot; selected according to the task and account access
Image, video and audio examples prepared by the instructor; a short live demonstration where generation quotas allow
Prepared evaluation, source and safe-use checklists
Word or Google Docs
2 days10 hours · net training time
Generative AI in Practice
A creative path from text and advanced prompting to images, video, music/audio, web and mobile design. Video and audio sessions consist of instructor demonstrations and output reviews; longer production workshops can be selected separately.
Who is it for?
Content creators, marketing and communications teams, educators, entrepreneurs and professionals using AI in creative work.
Prerequisites
Basic digital tool skills, prepared briefs and approved accounts. Participants do not all need paid accounts for the video/audio demonstrations.
Target outcome
A prompt set, text and image drafts, reviewed video/audio examples, web and mobile screen drafts, and an improved version of one selected output.
Explore the daily schedule
Day 1 · Generative AI, prompting and content foundations
5 hours · net training time
AI literacy and its evolution30 min · Concepts and discussion
How do large language models work?30 min · Demonstration and review
Foundational prompt engineering workshop1 hour · Guided practice
Safe use, source checks and output review45 min · Guided practice
Text, communication and content creation1 hour · Guided practice
Advanced prompting, task chains and prompt libraries1 hour 15 min · Guided practice
Day 2 · Exploring images, video, audio, web and mobile
5 hours · net training time
Image generation and visual prompts45 min · Guided practice
Video generation and storyboarding45 min · Demonstration and review
Music, audio and voice-over45 min · Demonstration and review
Web interface design with AI1 hour · Guided practice
Mobile app design with AI1 hour · Guided practice
Creative mini project and revision45 min · Guided practice
Tool options
A topic map, whiteboard or Miro; printed templates as an alternative
An approved assistant: ChatGPT, Claude, Gemini or Microsoft 365 Copilot; selected according to the task and account access
Prepared evaluation, source and safe-use checklists
Word or Google Docs
Canva or an approved image generation tool; an existing license and generation quota
Kling or an approved video generation tool; a prepared storyboard, short clip and generation quota
Image, video and audio examples prepared by the instructor; a short live demonstration where generation quotas allow
Music with Suno or voice-over with an approved TTS tool; voice owner/copyright permissions and quota
Figma or Google Stitch; a prepared web/mobile brief and an accessibility checklist
One tool and a prepared brief for the selected text, image, audio, video or interface output
5 days25 hours · net training time
AI 360°: From Foundations to Applications
Connect the AI landscape with technical foundations, creative tools, coding, automation, data, industry, law and ethics. Spend the final day on strategy, the Türkiye ecosystem and a clearly bounded team project.
Who is it for?
Managers, specialists, technology and business teams seeking a broad AI perspective, and participants deciding which field to explore in depth.
Prerequisites
Basic computer and digital tool skills. Technical subjects are covered through concepts and demonstrations; labs requiring coding are selected separately.
Target outcome
A comparison of technical and organizational AI fields, small practical outputs, a team solution outline/prototype and a 30–60–90-day learning or pilot roadmap.
Explore the daily schedule
Day 1 · The world of AI, tools and a safe start
5 hours · net training time
AI literacy and its evolution30 min · Concepts and discussion
AI fields and core concepts30 min · Concepts and discussion
How do large language models work?30 min · Demonstration and review
Foundational prompt engineering workshop1 hour · Guided practice
The AI tool ecosystem and multimodal use45 min · Demonstration and review
Safe use, source checks and output review45 min · Guided practice
How AI changes work and use cases30 min · Concepts and discussion
Your personal practice and learning path30 min · Guided practice
Day 2 · Machine learning, deep learning, NLP and vision
5 hours · net training time
Introduction to machine learning45 min · Concepts and discussion
ML algorithms and problem–model fit1 hour · Demonstration and review
Deep learning and neural networks45 min · Demonstration and review
Modern DL architectures and generative models45 min · Concepts and discussion
NLP, language and speech technologies45 min · Demonstration and review
Computer Vision and visual AI1 hour · Demonstration and review
Day 3 · Generative AI, knowledge assistants, automation and coding
5 hours · net training time
Knowledge management and assistants with NotebookLM45 min · Demonstration and review
Image generation and visual prompts45 min · Guided practice
Video generation and storyboarding45 min · Demonstration and review
AI-assisted business automation with n8n1 hour · Guided practice
Coding assistants and a vibe coding demonstration1 hour · Demonstration and review
AI agents, tools and multi-agent systems45 min · Demonstration and review
Day 4 · Data, industry, cybersecurity, law and ethics
5 hours · net training time
Big Data and data architectures45 min · Concepts and discussion
IoT, Edge AI and on-device AI45 min · Concepts and discussion
Digital twins, VR/AR and robotics45 min · Demonstration and review
AI in cyber defense45 min · Demonstration and review
Security of AI systems30 min · Concepts and discussion
AI ethics and trustworthy AI45 min · Concepts and discussion
The legal dimensions of AI and intellectual property45 min · Concepts and discussion
Day 5 · Policy, the Türkiye ecosystem, strategy and projects
5 hours · net training time
Corporate AI policy and governance45 min · Guided practice
Choosing tools, licenses, costs and resources30 min · Guided practice
Corporate AI strategy and business value30 min · Guided practice
The Türkiye AI ecosystem and current directions45 min · Concepts and discussion
Team project: from AI idea to solution outline2 hours · Guided practice
Project review and a 30–60–90-day plan30 min · Guided practice
Tool options
A topic map, whiteboard or Miro; printed templates as an alternative
An approved assistant: ChatGPT, Claude, Gemini or Microsoft 365 Copilot; selected according to the task and account access
Image, video and audio examples prepared by the instructor; a short live demonstration where generation quotas allow
Prepared evaluation, source and safe-use checklists
Word or Google Docs
Prepared data/charts and interactive classification, regression and clustering examples
Python, Jupyter and scikit-learn; prepared sample data with an appropriate license
Neural network visuals and a prepared PyTorch or TensorFlow example; a CPU or a pre-provisioned environment
Hugging Face model cards and examples of open-weight models with checked licenses
Prepared Turkish/multilingual text, embedding, sentiment analysis and speech processing examples
OpenCV or a prepared vision model; images approved for use and OCR/object detection examples
Gemini Notebook (NotebookLM) or an approved source-grounded knowledge tool; a prepared document set
Canva or an approved image generation tool; an existing license and generation quota
Kling or an approved video generation tool; a prepared storyboard, short clip and generation quota
A prepared n8n or Power Automate training environment; fictional data and human approval
Cursor, GitHub Copilot or an approved coding assistant; a small prepared project
Prepared, safe demonstrations of tool calling, memory, MCP/API connections and multiple agents
Architecture examples covering data lakes, warehouses, SQL, pipelines and stream processing
IoT sensor, Edge AI and manufacturing cases; sample data/simulation
Prepared videos/simulations for digital twins, VR/AR and robotics; physical hardware setup is not included
Anonymized security logs, phishing examples and attack/defense scenarios; an isolated training environment
Case cards on fairness, bias, explainability, privacy and environmental impact
Current KVKK, GDPR and European Commission AI Act sources; sample license/contract clauses
Excel or Google Sheets; a prepared Python notebook where needed
Dated primary sources from TÜBİTAK, public institutions, universities and startups; Turkish model cards
Prepared templates and tools suited to the creative, technical or business scenario chosen by the group
Case cards for finance, healthcare, retail, manufacturing, transport, HR, marketing, education and contact centers
Preparing for the training
Tools and learning formats
Practical exercises require a computer, an approved account and prepared sample files. Demonstrations use the instructor’s environment and outputs prepared in advance. Programming, data and setup prerequisites for technical labs are listed on the module cards.
The products listed are tool options; you do not need to purchase them all. Accounts, licenses, file uploads, quotas and network access are checked before the training. Budgets and stopping limits are set in advance, particularly for image/video/audio generation and API use. Exercises use fictional data or data approved for use.
Concept sessions introduce a field, demonstrations build the ability to assess examples, and practical exercises produce a small output. Technical labs focus on one problem in a prepared environment.
Outputs are assessed for accuracy, creativity, suitability for the task, sources, rights and safe use.
Every subject in the catalog can also be selected individually. Ready-made programs draw on this pool; they do not try to fit every module into one package. In-depth labs are planned separately and increase the total time when added.
60 modules shown
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AI literacy and the ecosystem30 min
AI literacy and its evolution
The evolution of AI, its current applications, opportunities and limits. The relationship between human judgment, automation and AI.
Concepts and discussion
Content and outcome
Target outcome
Break a task into AI, automation and human steps.
Prerequisites
Basic computer skills.
Tool options
A topic map, whiteboard or Miro; printed templates as an alternative
AI literacy and the ecosystem30 min
AI fields and core concepts
Symbolic AI, search/optimization, ML, DL, generative AI, LLMs and multimodal systems. Put narrow AI and general AI into perspective.
Concepts and discussion
Content and outcome
Target outcome
A concept map matching the main fields with examples.
Prerequisites
No specific technical prerequisites.
Tool options
A topic map, whiteboard or Miro; printed templates as an alternative
Generative AI and prompt design30 min
How do large language models work?
Tokens, context, training/inference, freshness and hallucinations. Products such as ChatGPT and model families such as GPT, Claude, Gemini and Llama; the difference between closed services and open-weight models.
Demonstration and review
Content and outcome
Target outcome
Explain the limitations of a model response and the available usage options.
Prerequisites
Basic AI concepts.
Tool options
An approved assistant: ChatGPT, Claude, Gemini or Microsoft 365 Copilot; selected according to the task and account access
Generative AI and prompt design1 hour
Foundational prompt engineering workshop
Role, task, context, sources, examples, output format and constraints. Assess and improve the first response against clear criteria.
Guided practice
Content and outcome
Target outcome
A tested, reusable prompt template.
Prerequisites
An approved AI account or a shared exercise with the instructor.
Tool options
An approved assistant: ChatGPT, Claude, Gemini or Microsoft 365 Copilot; selected according to the task and account access
Generative AI and prompt design1 hour 15 min
Advanced prompting, task chains and prompt libraries
Personas, few-shot examples, task decomposition, structured outputs and quality criteria. Compare and version prompts suited to the workflow.
Guided practice
Content and outcome
Target outcome
A three-step prompt workflow and a reviewed set of templates.
Prerequisites
Basic prompting skills.
Tool options
An approved assistant: ChatGPT, Claude, Gemini or Microsoft 365 Copilot; selected according to the task and account access
Prepared evaluation, source and safe-use checklists
AI literacy and the ecosystem45 min
The AI tool ecosystem and multimodal use
A map of tools for text, research, images, video, audio and code, running locally or in the cloud. Explore task–tool fit through two short demonstrations selected in advance.
Demonstration and review
Content and outcome
Target outcome
A tool map organized by task.
Prerequisites
No specific technical prerequisites.
Tool options
An approved assistant: ChatGPT, Claude, Gemini or Microsoft 365 Copilot; selected according to the task and account access
Image, video and audio examples prepared by the instructor; a short live demonstration where generation quotas allow
Law, ethics and governance45 min
Safe use, source checks and output review
Personal data, trade secrets, source verification, deepfakes and malicious instructions in documents. Which data can be shared and when human approval is needed.
Guided practice
Content and outcome
Target outcome
A corrected example and a safe-use checklist.
Prerequisites
Basic AI concepts.
Tool options
An approved assistant: ChatGPT, Claude, Gemini or Microsoft 365 Copilot; selected according to the task and account access
Prepared evaluation, source and safe-use checklists
AI literacy and the ecosystem30 min
How AI changes work and use cases
Changing tasks within professions, human–AI collaboration, learning needs and realistic expectations. Examples from different business functions.
Concepts and discussion
Content and outcome
Target outcome
Identify two opportunities and one limitation for your own role.
Prerequisites
A work or everyday task familiar to the participant.
Tool options
A topic map, whiteboard or Miro; printed templates as an alternative
Strategy, projects and transformation30 min
Your personal practice and learning path
Review a short trial; identify interests, skill gaps, resources and next steps. Build habits for daily learning and safe use.
Guided practice
Content and outcome
Target outcome
A learning plan with two practice goals and a follow-up measure.
Prerequisites
Introductory sessions or equivalent knowledge.
Tool options
Word or Google Docs
Prepared evaluation, source and safe-use checklists
Generative AI and prompt design1 hour
Text, communication and content creation
Examples of emails, proposals, customer replies, meeting summaries, LinkedIn/marketing and training content. Audience, tone, factual accuracy and brand voice.
Guided practice
Content and outcome
Target outcome
A revised text and an alternative version for one selected scenario.
Prerequisites
Basic prompting skills and a sample brief.
Tool options
An approved assistant: ChatGPT, Claude, Gemini or Microsoft 365 Copilot; selected according to the task and account access
Word or Google Docs
Generative AI and prompt design1 hour
Research, document analysis and comparison
PDF/Word review, industry/competitor research and client briefings. Claim–source–date relationships, contradictions and missing information.
Guided practice
Content and outcome
Target outcome
A comparison or research summary with traceable sources.
Prerequisites
Documents approved for use and basic prompting skills.
Tool options
An approved assistant: ChatGPT, Claude, Gemini or Microsoft 365 Copilot; selected according to the task and account access
Gemini Notebook (NotebookLM) or an approved source-grounded knowledge tool; a prepared document set
Knowledge assistants and agents45 min
Knowledge management and assistants with NotebookLM
Questions, answers, summaries and citations using prepared sources in Gemini Notebook/NotebookLM. Review an audio summary example for source fidelity and accuracy.
Demonstration and review
Content and outcome
Target outcome
A question/source list for evaluating a knowledge assistant’s answers.
Prerequisites
A document set prepared in advance; available tool features are checked against access.
Tool options
Gemini Notebook (NotebookLM) or an approved source-grounded knowledge tool; a prepared document set
Images, video, music and audio45 min
Image generation and visual prompts
Posters, covers, product concepts and social media visuals. Composition, style, branding, editing and usage rights through one small example.
Guided practice
Content and outcome
Target outcome
An image draft and a brief for improvement.
Prerequisites
A prepared brief, an approved tool and a quota.
Tool options
Canva or an approved image generation tool; an existing license and generation quota
Images, video, music and audio45 min
Video generation and storyboarding
Scenes, camera, continuity and video prompts with Kling or a selected tool. An instructor demonstration using a prepared short clip and storyboard, followed by a review of quality and rights.
Demonstration and review
Content and outcome
Target outcome
A storyboard draft and revision notes for a sample clip.
Prerequisites
A paid participant account is not required; the instructor’s environment is prepared in advance.
Tool options
Kling or an approved video generation tool; a prepared storyboard, short clip and generation quota
Image, video and audio examples prepared by the instructor; a short live demonstration where generation quotas allow
Images, video, music and audio45 min
Music, audio and voice-over
The difference between music/jingles with Suno and voice-over with TTS. Rhythm, brand tone, vocal/instrumental generation, voice owner permission and copyright; review prepared examples.
Demonstration and review
Content and outcome
Target outcome
A music or voice-over brief and an evaluation note.
Prerequisites
Instructor-prepared examples; explicit consent if a person’s voice will be used.
Tool options
Music with Suno or voice-over with an approved TTS tool; voice owner/copyright permissions and quota
Image, video and audio examples prepared by the instructor; a short live demonstration where generation quotas allow
Web, mobile and product design1 hour
Web interface design with AI
Landing page purpose, information hierarchy, CTAs, visual language and accessibility. Draft a single page from a prepared brief in Figma/Stitch.
Guided practice
Content and outcome
Target outcome
A screen draft for one web page.
Prerequisites
Basic digital tool skills and a prepared brief.
Tool options
Figma or Google Stitch; a prepared web/mobile brief and an accessibility checklist
Web, mobile and product design1 hour
Mobile app design with AI
Product idea, user flow, onboarding, home screen and paywall principles. One mobile flow from a prepared brief; the difference between a design prototype and a working product.
Guided practice
Content and outcome
Target outcome
A screen list and home screen draft for one mobile flow.
Prerequisites
Basic digital tool skills and an app idea/brief.
Tool options
Figma or Google Stitch; a prepared web/mobile brief and an accessibility checklist
Web, mobile and product design45 min
Creative mini project and revision
Choose a text, image, storyboard or interface draft and improve it against the brief. Plan how separate media outputs would come together in a campaign.
Guided practice
Content and outcome
Target outcome
One revised output and a task list for the other assets.
Prerequisites
At least one draft prepared in earlier sessions.
Tool options
One tool and a prepared brief for the selected text, image, audio, video or interface output
Prepared evaluation, source and safe-use checklists
Knowledge assistants and agents1 hour
AI-assisted business automation with n8n
Trigger, data, LLM step, human approval and output. Process fictional data in a prepared n8n/Power Automate template, with error handling and cost limits.
Guided practice
Content and outcome
Target outcome
A small adapted workflow with an approval step in the training environment.
Prerequisites
An account/template already working and access checks completed.
Tool options
A prepared n8n or Power Automate training environment; fictional data and human approval
Knowledge assistants and agents45 min
AI agents, tools and multi-agent systems
Agents versus fixed automation; tool calling, memory, planning and division of work among agents. A prepared demonstration of MCP/API connections and permission boundaries.
Demonstration and review
Content and outcome
Target outcome
Determine whether a task needs an agent and where human approval belongs.
Prerequisites
Basic LLM and automation concepts.
Tool options
A prepared n8n or Power Automate training environment; fictional data and human approval
Prepared, safe demonstrations of tool calling, memory, MCP/API connections and multiple agents
Knowledge assistants and agents30 min
Comparing RAG, context and fine-tuning
Adding documents to context, embeddings/retrieval, RAG and model adaptation. Open-weight models, local execution, and data/compute requirements.
Concepts and discussion
Content and outcome
Target outcome
Choose and justify suitable approaches for three scenarios.
Prerequisites
LLM and data concepts.
Tool options
Gemini Notebook (NotebookLM) or an approved source-grounded knowledge tool; a prepared document set
Hugging Face model cards and examples of open-weight models with checked licenses
Software development with AI1 hour
Coding assistants and a vibe coding demonstration
Draft a small feature with Cursor, GitHub Copilot or an approved assistant. Task definition, reading code, debugging and safe execution boundaries.
Demonstration and review
Content and outcome
Target outcome
Explain a coding assistant workflow and the role of human review.
Prerequisites
Coding is not required; the instructor demonstrates a prepared project.
Tool options
Cursor, GitHub Copilot or an approved coding assistant; a small prepared project
Software development with AI2 hours
AI-assisted software development practice
Develop a feature in a small prepared project, debug it, review the Git diff and run focused checks. A controlled workflow from requirement to code change.
Guided practice
Content and outcome
Target outcome
A small feature that runs locally and a review note.
Prerequisites
Basic programming, Git and a configured development environment.
Tool options
Cursor, GitHub Copilot or an approved coding assistant; a small prepared project
Git, code diffs and a review checklist
Software development with AI1 hour 30 min
Model APIs and application integration
HTTP/JSON, credential security, structured outputs, error handling and quotas. Connect a local API mock to a prepared Python/JavaScript application.
Guided practice
Content and outcome
Target outcome
A working sample integration and a usage limit.
Prerequisites
Basic programming and HTTP/JSON; a budget is approved in advance if live access is needed.
Tool options
A prepared Python/JavaScript client and a local API mock; a quota approved in advance if live model access is available
Cursor, GitHub Copilot or an approved coding assistant; a small prepared project
Software development with AI1 hour
Quality, testing and security in AI-generated code
Read generated code against requirements, apply meaningful checks, examine dependency/secret risks, debug and manage versions.
Guided practice
Content and outcome
Target outcome
A review and acceptance record for one code change.
Prerequisites
Basic programming and a prepared sample project.
Tool options
Cursor, GitHub Copilot or an approved coding assistant; a small prepared project
Git, code diffs and a review checklist
Prepared evaluation, source and safe-use checklists
Machine learning and deep learning45 min
Introduction to machine learning
Supervised, unsupervised and reinforcement learning; classification and regression. The logic of data, features, targets, training and evaluation.
Concepts and discussion
Content and outcome
Target outcome
Match business problems with types of learning.
Prerequisites
Basic AI concepts; no coding required.
Tool options
Prepared data/charts and interactive classification, regression and clustering examples
Machine learning and deep learning1 hour
ML algorithms and problem–model fit
Linear/logistic regression, decision trees, Random Forest, SVM and KNN. Compare metrics, overfitting and model selection using prepared results.
Demonstration and review
Content and outcome
Target outcome
Choose a starting model and an evaluation criterion for one problem.
Prerequisites
Introduction to ML and basic chart-reading skills.
Tool options
Python, Jupyter and scikit-learn; prepared sample data with an appropriate license
Prepared data/charts and interactive classification, regression and clustering examples
Machine learning and deep learning45 min
Clustering, recommender systems and reinforcement learning
Grouping unlabeled data, similarity/recommendations, rewards and policies. Explore task fit and inappropriate uses through prepared examples.
Demonstration and review
Content and outcome
Target outcome
Match three use cases with methods and data requirements.
Prerequisites
Basic ML concepts.
Tool options
Python, Jupyter and scikit-learn; prepared sample data with an appropriate license
Prepared data/charts and interactive classification, regression and clustering examples
Machine learning and deep learning45 min
Deep learning and neural networks
Neurons/layers, activation, loss and the idea of backpropagation. Use prepared visuals to see how learning progresses and explore data and GPU/CPU needs.
Demonstration and review
Content and outcome
Target outcome
Explain a neural network training loop in simple terms.
Prerequisites
Basic ML concepts; no mathematical derivations or coding required.
Tool options
Neural network visuals and a prepared PyTorch or TensorFlow example; a CPU or a pre-provisioned environment
Machine learning and deep learning45 min
Modern DL architectures and generative models
A map of CNNs, RNNs/LSTMs, Transformers, GANs and diffusion. Transfer learning and fine-tuning; compare architectures by application.
Concepts and discussion
Content and outcome
Target outcome
A map connecting architectures, data types and tasks.
Prerequisites
Deep learning foundations.
Tool options
Neural network visuals and a prepared PyTorch or TensorFlow example; a CPU or a pre-provisioned environment
Hugging Face model cards and examples of open-weight models with checked licenses
Machine learning and deep learning45 min
NLP, language and speech technologies
Tokenization, embeddings, text classification, sentiment analysis, summarization, chatbots and question answering. Turkish data and ASR/TTS concepts.
Demonstration and review
Content and outcome
Target outcome
Define an approach and an evaluation criterion for a language/speech task.
Prerequisites
Basic model concepts; demonstrations use prepared examples.
Tool options
Hugging Face model cards and examples of open-weight models with checked licenses
Prepared Turkish/multilingual text, embedding, sentiment analysis and speech processing examples
Machine learning and deep learning1 hour
Computer Vision and visual AI
Image classification, object detection, OCR, the limits of face recognition and quality control. Assess errors and privacy using examples approved for use.
Demonstration and review
Content and outcome
Target outcome
A capability and risk assessment for a visual use case.
Prerequisites
Basic model concepts; no exercises using personal/biometric data.
Tool options
OpenCV or a prepared vision model; images approved for use and OCR/object detection examples
Big Data and MLOps45 min
Big Data and data architectures
The 5 Vs, data lakes, warehouses, batch/real-time processing and data pipelines. Connections between SQL, data engineering and AI projects.
Concepts and discussion
Content and outcome
Target outcome
An architecture map of a sample data flow.
Prerequisites
Basic data/spreadsheet knowledge.
Tool options
Architecture examples covering data lakes, warehouses, SQL, pipelines and stream processing
Big Data and MLOps45 min
Data quality, features and data leakage
Missing values, outliers, duplicate records, feature selection and data splitting. Examine the effect of data quality on model results in a prepared example.
Guided practice
Content and outcome
Target outcome
A data preparation checklist and a corrected small example.
Prerequisites
Basic spreadsheet knowledge; basic Python for the technical path.
Tool options
Excel or Google Sheets; a prepared Python notebook where needed
Python, Jupyter and scikit-learn; prepared sample data with an appropriate license
Machine learning and deep learning45 min
An end-to-end mini ML demonstration
Business problem, sample data, training/validation/test splits, metrics and error review. Follow a prepared classification or regression notebook.
Demonstration and review
Content and outcome
Target outcome
Explain the steps and limitations of a model experiment.
Prerequisites
Introduction to ML; no coding required.
Tool options
Python, Jupyter and scikit-learn; prepared sample data with an appropriate license
Industrial and sector applications45 min
IoT, Edge AI and on-device AI
Sensor data, latency, offline operation, energy and the division of work between cloud and device. Manufacturing and maintenance examples; model size and update constraints.
Concepts and discussion
Content and outcome
Target outcome
An edge/cloud placement decision for one use case.
Prerequisites
Basic AI and data concepts.
Tool options
IoT sensor, Edge AI and manufacturing cases; sample data/simulation
Industrial and sector applications45 min
Digital twins, VR/AR and robotics
Digital twins versus simulations; VR/AR experiences, perception/decision/action and AI in robotics. Explore feasibility through prepared examples.
Demonstration and review
Content and outcome
Target outcome
A technology and data requirements map for a physical/digital scenario.
Prerequisites
An awareness session; no hardware or field setup required.
Tool options
Prepared videos/simulations for digital twins, VR/AR and robotics; physical hardware setup is not included
Cybersecurity and AI security45 min
AI in cyber defense
Anomaly, phishing and malware detection in security logs. False positives, analyst support and human judgment; demonstrations with anonymized examples.
Demonstration and review
Content and outcome
Target outcome
An assessment of benefits and false positives for a defense scenario.
Prerequisites
Basic security concepts are helpful; examples are prepared and isolated.
Tool options
Anonymized security logs, phishing examples and attack/defense scenarios; an isolated training environment
Cybersecurity and AI security30 min
Security of AI systems
Prompt injection, data/model leakage, adversarial examples, data poisoning and agent tool permissions. Layers of defense and incident reporting.
Concepts and discussion
Content and outcome
Target outcome
A risk–control mapping for one AI system.
Prerequisites
Basic model/agent concepts; no hands-on attacks.
Tool options
Anonymized security logs, phishing examples and attack/defense scenarios; an isolated training environment
Prepared evaluation, source and safe-use checklists
Law, ethics and governance45 min
AI ethics and trustworthy AI
Bias, fairness, explainability, privacy, deepfakes/manipulation and sustainability. Human oversight and conflicts between values through a case.
Concepts and discussion
Content and outcome
Target outcome
A reasoned decision and proposed controls for an ethical case.
Prerequisites
No specific technical prerequisites.
Tool options
Case cards on fairness, bias, explainability, privacy and environmental impact
Law, ethics and governance45 min
The legal dimensions of AI and intellectual property
KVKK/GDPR, EU AI Act awareness, data transfers, copyright, licenses, liability and contracts. Distinguish areas of responsibility using current sources.
Concepts and discussion
Content and outcome
Target outcome
Questions and checkpoints to clarify with the legal team.
Prerequisites
An awareness session; organization-specific legal advice requires a separate expert review.
Tool options
Current KVKK, GDPR and European Commission AI Act sources; sample license/contract clauses
Law, ethics and governance45 min
Corporate AI policy and governance
Data classes, approved tools, approval roles, risk matrices, records and incident reporting. Adapt a prepared policy template to a scenario.
Guided practice
Content and outcome
Target outcome
A policy draft and responsibilities list for internal review.
Prerequisites
Existing organizational rules or a prepared sample policy.
Tool options
Current KVKK, GDPR and European Commission AI Act sources; sample license/contract clauses
Word or Google Docs
Strategy, projects and transformation30 min
Choosing tools, licenses, costs and resources
Task fit, open/closed models, licenses, data boundaries, API/generation quotas and total cost. Spending alerts, stopping limits and an alternative plan.
Guided practice
Content and outcome
Target outcome
A cost/access comparison of two options.
Prerequisites
Prepared usage and pricing examples; actual prices are refreshed before the training.
Tool options
Excel or Google Sheets; a prepared Python notebook where needed
Prepared evaluation, source and safe-use checklists
Strategy, projects and transformation30 min
Corporate AI strategy and business value
AI Canvas, a use-case pool, impact/feasibility/risk and quick wins. ROI assumptions, baseline measurements and human effort.
Guided practice
Content and outcome
Target outcome
Prioritized scenarios and one measure of benefit.
Prerequisites
A business process familiar to the participant.
Tool options
A topic map, whiteboard or Miro; printed templates as an alternative
Excel or Google Sheets; a prepared Python notebook where needed
Industrial and sector applications45 min
A map of AI across sectors
Finance, healthcare, retail, manufacturing, transport, HR, marketing, education and contact centers. How data, risks and value change when similar technologies are used.
Concepts and discussion
Content and outcome
Target outcome
A comparative map of use cases across sectors.
Prerequisites
No specific technical prerequisites.
Tool options
Case cards for finance, healthcare, retail, manufacturing, transport, HR, marketing, education and contact centers
Strategy, projects and transformation45 min
AI project management and feasibility
Problem and user, data discovery, suitability of AI, team roles, dependencies, acceptance criteria and project risks.
Guided practice
Content and outcome
Target outcome
A pilot project brief with clear scope.
Prerequisites
A selected business or product idea.
Tool options
A topic map, whiteboard or Miro; printed templates as an alternative
Big Data and MLOps45 min
MLOps, LLMOps and the move to production
Versioning, evaluation/monitoring, an A/B approach, drift, retraining, costs, rollback and technical debt. A prepared model/LLM lifecycle example.
Demonstration and review
Content and outcome
Target outcome
A checklist for moving a prototype to production.
Prerequisites
Basic ML or LLM application knowledge; environment setup is not included.
Tool options
Prepared model versioning, evaluation, monitoring, drift and cost dashboards; examples of tools such as MLflow
AI literacy and the ecosystem45 min
The Türkiye AI ecosystem and current directions
Startups, university/public initiatives, Turkish models and local use cases in Türkiye. Evaluate directions such as Agentic AI and open models using dated sources.
Concepts and discussion
Content and outcome
Target outcome
An ecosystem/learning map with dates and sources.
Prerequisites
No specific technical prerequisites; examples are updated before the training.
Tool options
Dated primary sources from TÜBİTAK, public institutions, universities and startups; Turkish model cards
Strategy, projects and transformation2 hours
Team project: from AI idea to solution outline
Choose a creative content, knowledge assistant, automation or sector case path. Problem, data, tool, rights/risks and success measures; one draft/prototype in a prepared environment.
Guided practice
Content and outcome
Target outcome
One demonstrable output and a feasibility/trial note.
Prerequisites
A case selected in advance and prepared tools/data; even short setup tasks are completed before the training.
Tool options
Prepared templates and tools suited to the creative, technical or business scenario chosen by the group
Case cards for finance, healthcare, retail, manufacturing, transport, HR, marketing, education and contact centers
Strategy, projects and transformation30 min
Project review and a 30–60–90-day plan
Short team presentations, peer feedback, resources and next learning steps. With more than three teams, use parallel review stations.
Guided practice
Content and outcome
Target outcome
A revision list and a learning/pilot roadmap with clear responsibilities.
Prerequisites
A prepared project output and a review format suited to the number of groups.
Tool options
Word or Google Docs
Prepared evaluation, source and safe-use checklists
Machine learning and deep learning5 hours
ML lab with Python/Jupyter
Preparation, a baseline model, training/evaluation, overfitting and error analysis using one dataset. Interpret results and record reproducible steps.
Hands-on lab · In-depth
Content and outcome
Target outcome
A working notebook, a model experiment and an assessment of metrics.
Prerequisites
Basic Python, data handling/statistics and an environment configured in advance.
Tool options
Python, Jupyter and scikit-learn; prepared sample data with an appropriate license
Machine learning and deep learning5 hours
Deep learning lab: text or images
Choose one image or text task. Apply transfer learning or limited fine-tuning to a prepared network/model, with data splits and evaluation.
Hands-on lab · In-depth
Content and outcome
Target outcome
A recorded experiment and error analysis for one task.
Prerequisites
Python and ML foundations; a prepared CPU/GPU environment, licensed data and an approved resource budget.
Tool options
Neural network visuals and a prepared PyTorch or TensorFlow example; a CPU or a pre-provisioned environment
Prepared Turkish/multilingual text, embedding, sentiment analysis and speech processing examples
Hugging Face model cards and examples of open-weight models with checked licenses
Knowledge assistants and agents5 hours
Corporate RAG bot prototype lab
Document chunking, embeddings, retrieval, source-grounded answers, access design and knowing when not to answer. Evaluate a small document set using a prepared scaffold.
Hands-on lab · In-depth
Content and outcome
Target outcome
A RAG prototype working with limited sources and a question set.
Prerequisites
Python/JavaScript, API knowledge, documents approved for use and prepared model access/quota.
Tool options
A prepared Python/JavaScript scaffold, a sample document repository, retrieval and evaluation set
A prepared Python/JavaScript client and a local API mock; a quota approved in advance if live model access is available
Knowledge assistants and agents5 hours
Agent and API automation lab
Choose one email/calendar/CRM or file scenario in a test environment. Tool permissions, human approval, error handling, trials and budget limits.
Hands-on lab · In-depth
Content and outcome
Target outcome
One controlled agent/automation workflow and an acceptance record.
Prerequisites
HTTP/JSON and basic automation; prepared test accounts, an approved quota and access.
Tool options
A prepared n8n or Power Automate training environment; fictional data and human approval
A prepared Python/JavaScript client and a local API mock; a quota approved in advance if live model access is available
Prepared, safe demonstrations of tool calling, memory, MCP/API connections and multiple agents
Software development with AI5 hours
Software prototyping lab with AI
Develop one user flow in a small prepared web or app project. Code review, focused checks, debugging and a Git record.
Hands-on lab · In-depth
Content and outcome
Target outcome
A prototype that runs locally and a list of known limitations.
Prerequisites
Basic programming/Git, a configured environment and a prepared starter project.
Tool options
Cursor, GitHub Copilot or an approved coding assistant; a small prepared project
Git, code diffs and a review checklist
A prepared Python/JavaScript client and a local API mock; a quota approved in advance if live model access is available
Images, video, music and audio3 hours
Image generation and branding studio
From brief to production, visual consistency, editing and revision. A small set of visuals for one campaign, with rights and source checks.
Hands-on lab · In-depth
Content and outcome
Target outcome
A revised set of visuals and usage notes.
Prerequisites
Basic visual prompting, an approved account, brand assets and a quota set in advance.
Tool options
Canva or an approved image generation tool; an existing license and generation quota
Images, video, music and audio3 hours
Video production studio
Script, storyboard, generation and basic editing for one short video. Frame/scene consistency, audio use, rights and one revision round.
Hands-on lab · In-depth
Content and outcome
Target outcome
A short video draft and storyboard.
Prerequisites
A prepared video account/quota, images approved for use and a brief; clip length is agreed in advance.
Tool options
Kling or an approved video generation tool; a prepared storyboard, short clip and generation quota
Images, video, music and audio3 hours
Music and audio production studio
Choose either a jingle or voice-over path. Text, brand tone, generation, basic editing, copyright/permission checks and revision.
Hands-on lab · In-depth
Content and outcome
Target outcome
One revised audio output and a usage rights note.
Prerequisites
A prepared account/quota and brief; explicit consent for a real person’s voice.
Tool options
Music with Suno or voice-over with an approved TTS tool; voice owner/copyright permissions and quota
Industrial and sector applications5 hours
A full-day sector-specific AI workshop
Process discovery, a case, data/rights/risk analysis, practice, evaluation and a roadmap for the chosen sector. Draw on relevant specialisms from the full catalog.
Hands-on lab · In-depth
Content and outcome
Target outcome
A sector-specific solution outline/prototype and an implementation plan.
Prerequisites
Participation by a domain expert, a case/data approved in advance and tool access.
Tool options
Case cards for finance, healthcare, retail, manufacturing, transport, HR, marketing, education and contact centers
Prepared templates and tools suited to the creative, technical or business scenario chosen by the group
Industrial and sector applications5 hours
Computer Vision and industrial AI lab
One OCR, image classification or quality control task. Work with a prepared model and sample data; assess error costs, field constraints and results.
Hands-on lab · In-depth
Content and outcome
Target outcome
A working experiment and a feasibility note for one visual task.
Prerequisites
Basic Python/ML, a prepared environment and images approved for use; physical field installation is not included.
Tool options
OpenCV or a prepared vision model; images approved for use and OCR/object detection examples
IoT sensor, Edge AI and manufacturing cases; sample data/simulation
Python, Jupyter and scikit-learn; prepared sample data with an appropriate license
