AI Agents and Agentic AI with Python and Generative AI Syllabus

Full curriculum breakdown — modules, lessons, estimated time, and outcomes.

Overview: This course provides a comprehensive, hands-on introduction to building AI agents and agentic AI systems using Python and generative AI technologies. Designed for intermediate learners, it spans approximately 18 hours of content across six modules, combining theoretical foundations with practical implementation. Learners will gain experience in neural networks, natural language processing, computer vision, and deploying intelligent systems in production environments. Each module includes interactive labs, guided projects, and real-world applications to reinforce learning. The program concludes with a final project that integrates key concepts for building autonomous, intelligent agents.

Module 1: Foundations of Computing & Algorithms

Estimated time: 3 hours

Core concepts in computing and algorithmic thinking
Applying computational methods to solve engineering problems
Hands-on exercises in algorithm design and analysis
Interactive lab: Building practical computing solutions

Module 2: Neural Networks & Deep Learning

Estimated time: 4 hours

Introduction to neural networks and deep learning architectures
Review of key frameworks and tools used in practice
Implementing deep learning models with Python
Hands-on exercises applying neural network techniques

Module 3: AI System Design & Architecture

Estimated time: 3 hours

Principles of AI system design and modular architecture
Designing intelligent systems for automation and decision-making
Interactive lab: Building scalable AI solutions
Guided project work with instructor feedback

Module 4: Natural Language Processing

Estimated time: 2 hours

Applying NLP techniques using transformer models
Understanding attention mechanisms and language modeling
Hands-on exercises in text processing and analysis
Implementing prompt engineering for LLMs

Module 5: Computer Vision & Pattern Recognition

Estimated time: 4 hours

Core concepts in computer vision and image recognition
Applying pattern recognition techniques with Python
Hands-on exercises using real-world datasets
Discussion of best practices and industry standards

Module 6: Final Project