In this module, we will introduce the course and provide an overview of the instructor’s background in AI and Python. We will explore the course objectives and structure to ensure you know what to expect. Additionally, we’ll guide you through the essential system setup, including installing tools like Python, an IDE, and managing API keys for the hands-on coding exercises.

In this module, we will explore the foundational concepts of Large Language Models (LLMs) and how they function within the AI space. We will compare traditional NLP techniques with LLMs to understand their advancements. Additionally, we will evaluate the real-world achievements and performance of these models across different tasks.

In this module, we will dive deep into the training process of Large Language Models, uncovering the complexities of data preparation and optimization techniques. We will explore ways to improve model performance and evaluate major LLM providers and their products. Additionally, you will learn how to interact with different LLMs via hands-on coding exercises.

In this module, we will explore various types of Large Language Models, including how to run models locally on your system. You will also dive into multimodal models, which combine text, images, and other media to enhance AI capabilities. Additionally, we will look at tokenization methods and how they support AI systems in processing and understanding data inputs.

In this module, we will introduce you to the concept of chains in AI, where multiple model interactions are linked together to form complex workflows. You will learn how to design and implement prompt templates for repeated use cases, and create systems where outputs are structured and can adapt based on different decision branches in your application.

In this module, we will explore vector databases and their significance in managing and retrieving high-dimensional data for AI applications. You will learn to work with vector embeddings, chunk data for more efficient storage, and practice querying databases to retrieve relevant information based on similarity searches.

In this module, we will introduce you to Retrieval-Augmented Generation (RAG) and walk you through its core phases, from data retrieval to response generation. You will gain hands-on experience in coding a basic RAG pipeline, enhancing the accuracy and relevance of the AI outputs by incorporating external information into the model’s process.

In this module, we will take a deeper dive into advanced techniques for enhancing Retrieval-Augmented Generation workflows. You will learn how to optimize data retrieval and refine responses with strategies like query expansion, prompt compression, and speculative RAG. Additionally, we will explore multimodal RAG and hybrid approaches to handle diverse data types efficiently.

In this module, we will introduce you to AI agents and the fundamental concepts behind agentic systems. We will explore frameworks used to build these systems and examine their potential applications in solving complex tasks autonomously. This module will set the stage for building more sophisticated AI-driven solutions in the following lessons.

In this module, we will focus on the crewAI framework, where you’ll learn how to work with agents to build powerful AI systems. We’ll guide you through the process of setting up a crewAI project, defining tasks, and debugging agent workflows. Additionally, you will extend these systems by integrating custom tools and ensuring smooth execution through testing.

In this module, we will dive into AG2, a powerful framework for building conversational AI agents. You will learn to code systems with multiple agents interacting with each other and with humans. Additionally, we’ll explore how to integrate external tools to extend the functionality of your agents and create more dynamic and adaptable AI systems.

In this module, we will explore the OpenAI Agents SDK and its features for building complex AI systems. You’ll learn how to create workflows that handle agent handoffs and ensure smooth operation. The course will also cover essential techniques for applying guardrails, ensuring safe agent behavior, and using tracing for debugging and performance monitoring.

In this module, we will introduce the Google Agent Development Kit (ADK) and guide you through building multi-agent systems. You will learn to work with function tools to extend agent capabilities and tackle complex tasks. This will enhance your ability to design sophisticated agent-driven workflows with the ADK framework.

In this module, we will focus on agent-to-agent communication protocols like MCP, A2A, and ACP. You will gain hands-on experience in setting up and testing MCP server-client interactions to facilitate effective communication between agents. This will equip you with the skills to build more dynamic and interconnected agent systems.

In this module, we will introduce you to model finetuning techniques, focusing on methods like LoRA. You’ll learn how to adapt pre-trained models to specific tasks and fine-tune their performance for better results. This skill will be crucial for optimizing AI models to meet the needs of different applications.