Live-Online instructor Led Training
GEN AI Foundations
Certificate
Get Hands-On with the Core Building Blocks of Generative AI -Enrollment opens in September 2025.
GEN AI Foundations
At a Glance
8
Modules
5
Weeks
50+
Hours of Training
Learn how to Transform Workflows with
Practical GenAI Solutions!
A hands-on course for engineers, analysts, and data professionals to build and deploy Generative AI solutions with Python. Learn core GenAI concepts, work with LLMs, create RAG systems, and develop real-world applications through live coding and mini-projects.
TRAINING COHORT
PART-TIME
DURATION
5 WEEKS
LIVE-ONLINE INSTRUCTOR LED TRAINING
HANDS ON PRODUCT
FRAMEWORKS AND USE CASES
Course Curriculum
About
The module is designed to equip technical product managers, software engineers, analysts, and data professionals with practical skills to build, integrate, and deploy Generative AI solutions using Python. Through structured modules, participants will learn foundational GenAI concepts, gain experience working with Large Language Models (LLMs), build Retrieval-Augmented Generation (RAG) systems using additional data sources, and develop deployable AI applications. The course emphasizes applied learning through live coding sessions, guided labs, and mini-projects—ensuring that participants not only understand how GenAI works but can also implement it in real-world business contexts.
Students Characteristics & Proficiency Skill Set
- Basic proficiency in Python (e.g., writing functions, using lists/dictionaries, basic loops)
- Familiarity with Jupyter notebooks or any code editor (ability to run and modify Python code)
- Basic understanding of data structures (no need for machine learning background)
- Comfort with reading technical documentation (e.g., Python libraries or APIs)
Python for Generative AI
This module ensures all participants have the foundational Python skills required to work with GenAI. Participants will refresh key programming concepts and gain familiarity with tools like Jupyter notebooks and libraries essential for data manipulation and visualization.
Trainer
Marion Nikoloudaki
Head of Data, AI/ML ITT - Lead Gen AI Trainer, Product-Led Hub
Foundations of Generative AI
This module covers the fundamental principles behind generative AI, including how large language models (LLMs) work, what embeddings are, and the differences between various model architectures. Participants will develop a strong conceptual understanding of how GenAI systems operate and where they are most effectively applied.
Trainer
Marion Nikoloudaki
Head of Data, AI/ML ITT - Lead Gen AI Trainer, Product-Led Hub
Working with LLMs using Python
Participants will learn how to interact with and extract value from large language models using Python. This includes crafting effective prompts, building generation pipelines, and integrating external tools or data sources for enhanced AI responses.
Trainer
Marion Nikoloudaki
Head of Data, AI/ML ITT - Lead Gen AI Trainer, Product-Led Hub
Retrieval-Augmented Generation (RAG) with Python
This module introduces RAG techniques, which allow LLMs to draw from external knowledge bases. Participants will build practical pipelines that enhance model responses by incorporating their own documents and structured data, improving both relevance and accuracy.
Trainer
Marion Nikoloudaki
Head of Data, AI/ML ITT - Lead Gen AI Trainer, Product-Led Hub
Building GenAI Apps
Participants will apply what they’ve learned to build real-world GenAI tools such as chatbots and content summarizers. This module focuses on integrating AI capabilities with user interfaces and deploying applications using modern frameworks.
Trainer
Marion Nikoloudaki
Head of Data, AI/ML ITT - Lead Gen AI Trainer, Product-Led Hub
Product Discovery for RAG
Define clear, focused specifications for a Minimum Viable Product (MVP) in AI-powered products. We’ll cover aligning business goals with technical feasibility, selecting the right AI scope for an MVP, balancing iteration speed with responsible AI use, and writing lean, effective specs that guide cross-functional teams from concept to first release.
Trainer
Ioannins Papikas
CTO at E-Satisfaction / Lead Product rainer, Product-Led Hub
Ethics, Evaluation & Future Trends
This module explores the ethical challenges and evaluation methods in GenAI, including how to detect hallucinations and bias. Participants will also gain insights into the regulatory landscape and future developments in AI, such as multimodal systems and on-device models.
Trainer
Marion Nikoloudaki
Head of Data, AI/ML ITT - Lead Gen AI Trainer, Product-Led Hub
Final Project
Participants will work independently or in small teams to develop and present a complete GenAI application. The project reinforces all prior learning and simulates a real-world build-and-deploy scenario using open data or documents.
Trainer
Marion Nikoloudaki
Head of Data, AI/ML ITT - Lead Gen AI Trainer, Product-Led Hub
Drive AI Transformation Through Practical
Team Training
Equip your engineering, product, and data teams with hands-on expertise to build and deploy real-world GenAI solutions using Python. This course delivers applied fluency in frameworks like LangChain, Hugging Face, and FAISS—empowering teams to develop LLM-powered applications, RAG systems, and scalable AI tools tailored to your domain.
Through live coding, labs, and mini-projects, participants master advanced prompting, semantic search, and data integration—accelerating your organization’s journey from GenAI experimentation to production-ready deployment.
Become a GenAI-Ready Expert
Take your career to the next level with in-demand, hands-on GenAI skills that go beyond theory. This program empowers you to build full-stack GenAI applications using Python and Large Language Models (LLMs), design advanced RAG pipelines and knowledge-integrated systems, and deploy AI solutions that are both high-performing and ethically sound.
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