Complete Gen AI: Basic to Agent AI, RAG, Bedrock, Vertex AI

Name: Complete Gen AI: Basic to Agent AI, RAG, Bedrock, Vertex AI
Rating: 4.1 (49 reviews)

Complete guide Gen AI with fundamentals of NLP, LangChain, LCEL, LangSmith, Agentic AI, RAG, Neo4J, Bedrock, Vertex AI

Created bySoumen Kumar Mondal

Last updated 8/2025

English

English [Auto],

What you'll learn

Master the fundamentals of NLP: Tokenization, embedding, POS tagging, TF-IDF, chunking, and more.
Understand the fundamentals of Generative AI: Explore key concepts like autoencoders, VAEs, GANs, and Transformer models
Master Prompt Engineering: Learn techniques to design effective prompts for models like ChatGPT, including zero-shot, one-shot, and few-shot prompting.
Work with industry-leading tools: Explore cutting-edge Generative AI platforms like ChatGPT, Google Gemini, and Microsoft CoPilot for real-world applications.
Set up the environment for hands-on Generative AI applications: Implement RAG using Python, VS Code, and LangChain.
Work with LangChain and LangChain Ecosystem Libraries (LCEL): Build real-world Generative AI applications and explore the LangChain ecosystem.
Develop AI Agents: Understand and implement agents like Crew AI and AutoGen to automate complex tasks.
Implement Vector RAG and Graph RAG: Use Neo4j for advanced retrieval and data augmentation techniques.
Learn Self-Reflective RAG techniques: Understand how AI can reason and reflect on its own processes.
Practical Python skills for Generative AI: Start from the basics and progress to advanced AI development with Python and libraries like NLTK.
Build AI solutions from the ground up: Gain end-to-end knowledge of Generative AI, from basics to advanced implementations with LangChain and LCEL.
Generative AI with AWS Bedrock
Generative AI with Google Cloud Vertex AI
Hands on use case implementation with AWS Bedrock BOTO3
Google Cloud Vertex AI use case impelementation

Course content

24 sections • 99 lectures • 14h 48m total length

Introduction7:23
This comprehensive Generative AI Course covers a range of essential topics, from the Fundamentals of NLP and Generative AI to Python basics for beginners. You'll get hands-on experience with LangChain, LangSmith, and LangGraph, building real-world AI solutions. The course delves into advanced concepts like Agents (Crew AI, AutoGen) and Retrieval-Augmented Generation (RAG), including Vector RAG and Graph RAG with Neo4j, and explores Self-Reflective RAG. Interactive quizzes reinforce learning and provide a practical approach to mastering these cutting-edge AI technologies.

Overview of Generative AI: Easy explanation, Gen AI vs Predective AI10:20
Understand fundamental of Generative AI. Difference between predictive AI and Generative AI.
Generative AI - Models: Latent Space4:56
Gen AI Models: Auto Encoder and VAE6:11
Gen AI Models : GANs model4:02
Explore generative adversarial networks (GANs), where a generator and a discriminator engage in adversarial training to produce increasingly realistic data, from images to deepfake video and audio.
Transformer model - Detailed understanding with 2 examples12:06

What is NLP?1:39
NLP concepts: POS, NER, Chunking, BOW, TF-IDF and Embeddings7:10
Explore fundamental NLP concepts such as parts of speech (pos), named entity recognition (NER), chunking, bag of words, tf-idf, and embeddings with practical examples.
NLP concepts: Tokenization, Stemming, Lemmatization3:31
Break down text into tokens to enable embedding and similarity search for language models. Explain stemming and lemmatization as reducing words to base or dictionary forms to improve query matching.
NLP concepts: Evaluation of NLP5:47
Explore how NLP evaluation evolved from rule-based systems to RNN/LSTM and transformers, highlighting how ChatGPT and Gemini deliver rapid, context-aware answers through self-attention.

Prompt Engineering: Concepts, Key Elements, Different techniques10:41
Learn how prompt engineering drives generative ai by shaping context, role assignment, instruction clarity, and desired output to improve responses across zero-shot, few-shot, and instruction-based techniques.
Prompt engineering hands on with ChatGPT8:25
Explore hands-on prompt engineering with ChatGPT, learning zero-shot and few-shot prompts, role and context setups, and clear instruction and output formats to generate tailored trip plans.
Prompting through Groq UI6:33
Prompting through Gemini6:26
Gen AI through Microsoft Co Pilot3:14
Explore how Microsoft Copilot uses prompts to generate a day-by-day trip plan in tabular format and compare its image generation and interface with Gemini.

Concept of LangChain9:01
Learn LangChain as a framework to build your own chatbot by chaining interoperable components like prompts, LM calls, and database connections, enabling scalable AI applications.
Overview of LCEL(LangChain Expression Language)4:50
Discover how Lang Chain Expression Language (LCL) declaratively defines runnable chains of prompts, LMs, and parsers. Use LCL for asynchronous, parallel execution and streaming, optimizing runtime and readability.
Quick hands-on with LCEL14:46
Demonstrates building a Python LangChain program to understand and compare without and with LCL, using env keys, chat templates, chain invocation, and post-processing outputs to a file.
First LLM Application with LangChain6:00
First Streamlit Chatbot with LangChain6:55

Agentic AI - Concept and workflow3:05
Agentic AI - Overview and Key characteristics3:35
Definition of agents in AI perspective. Characteristics and purpose of characteristics of agentic AI.
Agentic AI - Applications2:58
Agentic AI has various applications and in future there will be more AI application will be developed with agentic AI concepts.
Agentic AI - Design Pattern9:49
Design pattern is important to get improvement from AI application. In this module we will learn all design patterns.

Requirements

Basic understanding of Python but dont worry the course will cover fundamental of Python.

Description

Unlock the full potential of Generative AI in this comprehensive, hands-on course tailored for students, developers, and AI enthusiasts. Whether you're a beginner or looking to deepen your expertise, this course offers an immersive experience, starting with the Fundamentals of Natural Language Processing (NLP) and Generative AI, giving you the foundational knowledge needed to excel. You will learn the basics of Python, ensuring even those new to programming can participate fully. From there, we dive into advanced LangChain implementations, where you'll build real-world applications. You'll also gain practical experience with LangSmith and LangGraph, key tools in the AI ecosystem.

Explore the power of AI Agents, including Crew AI and AutoGen, and see how these autonomous systems can transform tasks like customer service, automation, and more. The course also covers cutting-edge Retrieval-Augmented Generation (RAG) techniques, including Vector RAG and Graph RAG using Neo4j for enhanced search and data retrieval. A special focus on Self-Reflective RAG will introduce you to the next frontier of AI-driven reasoning.

With quizzes, practical coding challenges, and hands-on projects, this course ensures you gain both theoretical understanding and practical experience in the most important areas of Generative AI. Get ready to build AI solutions from the ground up!

Who this course is for:

Data Scientists
Machine Learning Engineers
AI and NLP Enthusiasts
Developers and Software Engineers
Researchers and Academics
Product Managers and Technical Leads
Students and Learners
AI Practitioners and Consultants
Quality Engineers

Complete Gen AI: Basic to Agent AI, RAG, Bedrock, Vertex AI

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 7min

Fundamental of Gen AI5 lectures • 38min

Fundamental of NLP4 lectures • 18min

Environment Setup1 lecture • 16min

Python (for Beginners)3 lectures • 37min

NLTK - Natural Language ToolKit : Understand NLP concept with Python2 lectures • 17min

Gen AI products and Prompt Engineering5 lectures • 35min

LangChain5 lectures • 42min

LangGraph1 lecture • 17min

Concept of Agentic AI4 lectures • 19min

Requirements

Description

Who this course is for: