Overview

A comprehensive diploma track designed to take learners from the fundamentals of programming to mastering cutting-edge Generative AI technologies. The curriculum begins with establishing a strong foundation in Python Programming and Object-Oriented Programming (OOP), before progressing to data visualization with Streamlit and database management. The core of the course focuses on Generative AI, including interfacing with Large Language Models (LLMs), Prompt Engineering, and building advanced AI applications using the LangChain Framework, RAG (Retrieval-Augmented Generation) architectures, and Lang-Graph for knowledge graph integration.

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Generative AI Diploma

Category:

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37

Enrollments

Level

All Levels

Time to Complete:

100 hours 0 minute

Lessons:

14

Certificate:

No

One-time for 1 person

8,500 EGP7,000 EGP
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Overview

A comprehensive diploma track designed to take learners from the fundamentals of programming to mastering cutting-edge Generative AI technologies. The curriculum begins with establishing a strong foundation in Python Programming and Object-Oriented Programming (OOP), before progressing to data visualization with Streamlit and database management. The core of the course focuses on Generative AI, including interfacing with Large Language Models (LLMs), Prompt Engineering, and building advanced AI applications using the LangChain Framework, RAG (Retrieval-Augmented Generation) architectures, and Lang-Graph for knowledge graph integration.

What You’ll Learn?

Master Python syntax, data structures, and Object-Oriented Programming principles.
Build interactive data visualizations and web apps using Streamlit.
Integrate databases with Python applications to store and retrieve records.
Understand the AI Ecosystem, Machine Learning basics, and the transition to Generative AI.
Build AI agents and custom chains using the LangChain Framework.
Implement RAG (Retrieval-Augmented Generation) pipelines to connect LLMs with external data.
Develop Graph-Augmented Generation systems using Lang-Graph.
Learn Prompt Engineering and how to interface with Large Language Models.
Mathematics for AI: Understand the core math behind AI, including Linear Algebra, Probability, and Statistics.
Machine Learning Mastery: Learn supervised and unsupervised learning algorithms, model evaluation, and feature engineering.
Deep Learning Fundamentals: Dive into Neural Networks, backpropagation, and activation functions using PyTorch or TensorFlow.
Generative AI & LLMs: Explore the architecture of Transformers, BERT, GPT, and how Large Language Models work.
Prompt Engineering: Master the art of crafting effective prompts to get the best results from AI models.
Advanced AI Frameworks: Build powerful AI applications using LangChain and LlamaIndex.
RAG Architecture: Implement Retrieval-Augmented Generation to create AI systems that can "read" and answer questions from your own data.
AI Deployment: Learn to deploy your models and build real-world AI agents.

Requirements

No specific prerequisites listed: The curriculum includes "Introduction to Python" and "Python Basics," implying it is open to beginners.

Syllabus Overview

14

Lessons

0

Quizzes

0

Tasks

0

Resources

Python Programming & OOP

Visualization with Streamlit

Working with Database

Project 1

Foundations of AI & Path to Generative AI

Introduction Generative AI

Interfacing with LLM

Prompt Engineering

Working with LangChain Framework

RAG Architecture

Project 2

Lang-Graph (Knowledge Graphs + LLMs)

Final Project

Material Includes

Assignment after each session.
Multiple Projects through the diploma, plus a final project.
Certificate accredited by The Egyptian Engineers Syndicate.

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