Minimum Criteria:

• Course Attendance - 80%
• Final Project / Exam - 20%

180 hours
February 18, 2026 - July 8, 2026

Training sessions will be held in person at Innovation Centre Kosovo (ICK)

The TechEco Pathways project, funded by LuxDev, aims to foster sustainable economic growth in Kosovo by addressing critical skill gaps in renewable energy and ICT.

The DevelopHer Kosova (Part of TechEco Pathways) program empowers young women in Kosovo through comprehensive technology training. It tackles youth unemployment and the gender gap in the tech industry by creating female role models with global opportunities. Its mission is to increase women’s participation in the ICT workforce and improve graduate employment rates.

This training is supported by the "Skills for Sustainable Jobs in Kosovo" project, funded by the Grand Duchy of Luxembourg and implemented by the Ministry of Education, Science, Technology and Innovation together with LuxDev, the Luxembourg Development Cooperation Agency.

• Applicants must be citizens of the Republic of Kosovo
• Applicants must be 18 years or older
• Unemployed and not in education individuals are encouraged to apply

This training program is a hands-on, industry-aligned curriculum designed to transform beginners into Junior AI / Machine Learning Engineers with practical cloud deployment skills.

• Structure: 3 Core Modules (AI, ML, Cloud Computing)
• Hours per Module: Targeted at 60 hours each, with flexibility to adjust based on class progress, learning speed, and newly emerging tools or techniques.
• Learning Philosophy: Learn → Build → Deploy → Secure → Ship
• Outcome: Participants will be able to design AI systems, train ML models, deploy them to the cloud, and release production-ready AI applications online.

The program emphasizes:

• Strong fundamentals
• Practical engineering skills
• Security and ethics
• Real-world AI application development
• Cloud-native deployment and cost awareness

Graduates will be prepared for junior roles in AI, ML, data, or cloud-focused teams

60 hours

February 18, 2026 - April 4, 2026

• Intro to Artificial Intelligence
• Narrow AI vs General AI
• AI in real-world applications (business, healthcare, security, finance)
• AI vs Machine Learning vs Deep Learning
• AI system lifecycle overview

• Intelligent agents and decision-making
• Rule-based systems
• Search algorithms (basic concepts)
• Knowledge representation
• Reasoning and inference
• Introduction to probabilistic thinking

• How AI uses Machine Learning
• Supervised vs Unsupervised vs Reinforcement Learning
• Neural networks basics
• Deep Learning fundamentals
• Large Language Models (LLMs) explained
• Where modern AI systems fit in the stack

•  AI development workflows
• AI APIs and platforms 
• Open-source vs proprietary AI tools (ChatGPT, Claude, Deepseek) 
• AI model hosting and inference concepts
• Introduction to vector databases 
• AI orchestration and pipelines (conceptual)

• Agentic AI & Designing autonomous AI agents
• Tool usage and decision loops
• Memory and context in AI systems
• Multi-agent systems overview
• Building a simple AI agent (hands-on) 
• AI applications: chatbots, assistants, automation

• Responsible AI principles
• Bias and fairness in AI systems 
• AI privacy considerations
• Prompt injection attacks
• Data leakage risks
• Model misuse and abuse scenarios
• Securing AI applications
• Governance and compliance overview

60 hours

April 6, 2026 - May 20, 2026

• Python fundamentals for ML
• Working with Jupyter notebooks
• Data types, functions, loops
• NumPy fundamentals
• Data manipulation basics
• Introduction to ML workflows in Python

• Data collection and cleaning
• Exploratory Data Analysis (EDA)
• Working with structured datasets
• Feature understanding and selection
• Handling missing and noisy data

• What is Machine Learning?
• Model training lifecycle
• Overfitting and underfitting
• Evaluation metrics
• Linear regression
• Logistic regression
• Binary classification tasks
• Model performance tuning

• Implementing ML models in Python
• Training supervised models
• Regression models for prediction
• Classification models
• Model evaluation and validation
• Hands-on ML exercises

• Introduction to open-source ML ecosystems
• Hugging Face ecosystem overview
• Pre-trained models
• Model inference vs training
• Retrieval-Augmented Generation (RAG)
• Connecting ML models with external data

• What is fine-tuning?
• When to fine-tune vs use pre-trained models
• Model versioning
• ML pipelines overview
• Intro to deploying ML models
• ML project mini-capstone

60 hours

May 23, 2026 - July 6, 2026

• Intro to Cloud Computing
• Cloud Computing for AI & ML
• Shared responsibility model

• IaaS, PaaS, SaaS explained
• Public cloud vs private cloud
• Hybrid and multi-cloud strategies
• Choosing the right cloud model

• Azure fundamentals
• AWS fundamentals
• Google Cloud fundamentals
• Core services comparison
• Cloud identity basics
• Cloud regions and availability zones

• Cloud architecture design principles
• Virtual networks
• Subnets and routing
• Load balancing concepts
• Networking for AI/ML workloads
• Secure network design

• Object storage, block storage, databases
• Compute services (VMs, containers, serverless)
• Storage selection for AI workloads
• Scaling strategies
• Performance considerations

• Securing cloud environments
• Identity and access management (IAM)
• Monitoring and logging basics
• Cost management and billing control
• Deploying AI/ML applications to cloud
• End-to-end AI app deployment project
• Cloud security basics
• Identity & Access Management (IAM)
• Role-based access control (RBAC)
• Secrets management
• Network security (security groups, firewalls)
• Securing AI workloads
• Data protection & encryption basics
• Cloud logging & monitoring concepts