Generative AI with Python

LLMs, Vector DBs, RAG, Agentic Systems, and more

What you'll learn
- Go beyond basic chatbots and learn to harness the intelligence of Large Language Models (LLMs) using Python.
- Discover how to create and leverage Vector Databases to store and efficiently retrieve information for your AI applications.
- Learn the cutting-edge technique that allows your AI to answer complex questions using your own data sources, making it smarter and more accurate.
- Explore the fascinating world of Agentic Systems and build autonomous AI agents that can perform tasks, make decisions, and interact with their environment.
- Get hands-on experience building practical projects that showcase the power and versatility of generative AI.
- Understand the fundamental concepts behind generative AI and gain the practical Python skills to bring your ideas to life.
- Acquire a deep understanding of the core technologies driving the next generation of intelligent applications.

Requirements
- Basic Python knowledge is required - you should know about basic data types, how to implement loops, or how to write functions.

Description
Unlock the transformative power of Generative AI with Python!This comprehensive course equips you with the essential knowledge and practical Python skills to master the core technologies driving this revolution, enabling you to build intelligent applications that understand, generate, and interact with language remarkably.

You'll delve into the fundamentals of Large Language Models (LLMs) and the crucial role of Vector Databases for efficient information retrieval. Discover the power of Retrieval-Augmented Generation (RAG), which allows your AI to answer complex questions using your own data, making it smarter and more contextually aware.

Furthermore, you'll explore the exciting domain of Agentic Systems, learning how to design and build autonomous AI agents capable of performing tasks and making decisions.

In my course I will teach you:

Large-Language Models

Classical NLP vs. LLM

Narrow AI Achievements

Model Performance and Achievements

Model Training Process

Model Improvement Options

Model Providers

Model Benchmarking

Interaction with LLMs

Message Types

LLM Parameters

Local Use of Models

Large Multimodal Models

Tokenization

Reasoning Models

Small Language Models

JailBreaking

Working with Chains

Parallel Chains, Router Chains, …

Vector Databases

Data Ingestion Pipeline

Data source and data loading

data chunking

embeddings

data storage

data querying

Retrieval-Augmented Generation

Baseline RAG

Context Enrichment

Corrective RAG

Hybrid RAG

Query Expansion

Speculative RAG

Agentic RAG

Agentic Systems

crewAI

Google ADK

OpenAI Agents SDK

AG2

Agent Interactions

MCP

ACP

A2A

Who this course is for:
- Python Programmers who want to expand their knowledge into the rapidly growing field of artificial intelligence and generative models.
More Info