Generative Ai Essentials - Practical Use Cases

Learn Generative AI Essentials with lectures, quizzes, real world use cases with hands-on demos and exercises

What you'll learn

AI Branches

Getting Started with Generative AI

Prompt Engineering, Anatomy and Framework

Generative Adversarial Networks (GANs)

Variational Autoencoder (VAE's)

Foundational Models (FMs)

Large Language Models (LLMs)

Retrieval Augmented Generation (RAG)

Trending Handson Use cases for Emails, PDF's, Text to Image & Video, Music Composition, Power Point Presentations, Designing Brand Logos, Data Analysis and more

AI - Best Practices

AWS Bedrock Project

Capstone Project - Your own GenAI application

Requirements

No prior experience is required

Laptop/Desktop with Internet Connectivity anfd that's it

Description

What's Covered in this Course?The "Generative AI Essentials - Practical Use Cases" course is tailored for learners of all levels, from beginners to seasoned professionals eager to explore the world of Generative AI through effective Prompt Engineering. This course serves as your gateway to mastering generative AI, offering essential concepts and practical, hands-on experience.We'll begin with an overview of Generative AI, tracing its evolution within the broader AI ecosystem, and then cover essential GenAI terminologies. Following this, we'll examine the fundamental concepts of prompt engineering and its best practices. The core of this course is practical use cases—domain-specific scenarios designed to streamline and enhance your daily workflows. We will also address the potential risks and challenges of using Generative AI. Finally, we'll conclude with an exciting capstone project utilizing AWS Bedrock.Whether you're new to AI or have some experience, this course will guide you through foundational concepts and each stage of learning.What is Generative AI?Generative AI is a subset of artificial intelligence capable of creating new content, including images, videos, music, text, code, and more. It achieves this by learning from extensive datasets of existing information and leveraging that knowledge to produce fresh, clean, and precise outputs.Legal Notice:ChatGPT is an open-source, community-driven software managed by the OpenAI. ChatGPT/OpenAI and the ChatGPT/OpenAI logo are trademarks or registered trademarks of The OpenAI in one or many countries. The OpenAI and other parties may also have trademark rights in other terms used herein. This course is not certified, accredited, affiliated with, nor endorsed by The OpenAI.Course Structure:LecturesDemosQuizzesAssignmentsCourse Contents:Getting Started with GenAIGenAI Terminologies:- Generative adversarial networks (GANs)- Variational Autoencoder (VAEs)- Foundational Models (FMs)- Large Language Models (LLMs)- Retrieval Augmented Generation (RAG)Prompt EngineeringHands-On Use Cases on GenAI Tools & Platforms:- Email Composition- Summarization- Pdf Reader | Chat with PDF- Content Creation: blogs, YouTube video etc- Text to Image- Text to Video- Creating Presentations- Product & Fashion Photo Shoots- Design Your Brand logo- Capture and Share Insights from Virtual Meetings- Coding and Development- Data Analysis- Building Website with Generative AI- Deploying Website with Generative AIResponsible Generative AIPotential RisksEthical ConsiderationsBest PracticesCapstone Project using AWS BedrockAll sections of this course are demonstrated live, providing step-by-step guidance to help you set up your local environment, perform all exercises, and learn through hands-on practice.

Overview

Section 1: Getting Started with GenAI Essentials

Lecture 1 Course Introduction

Lecture 2 Course Handbook - Generative AI Essentials - Practical Use Cases

Lecture 3 Understanding AI Branches

Lecture 4 Introduction to Generative AI (GenAI) and its Evolution

Lecture 5 Application Field & Trends of GenAI

Lecture 6 Checkout - AI Ecosystem for the Absolute Beginners Course

Lecture 7 Optional - AI Ecosystem Layers

Section 2: GenAI Terminologies

Lecture 8 Generative Adversarial Networks (GANs)

Lecture 9 Variational AutoEncoder (VAEs)

Lecture 10 Foundational Models (FMs)

Lecture 11 Large Language Models (LLMs)

Lecture 12 Retrieval Augmented Generation (RAG)

Lecture 13 Demo - GenAI Toolset

Section 3: Prompt Engineering

Lecture 14 What is Prompt Engineering?

Lecture 15 Prompt Anatomy and Best Practices

Lecture 16 Prompt Anatomy with ChatGPT

Lecture 17 Fine Tuning

Lecture 18 Demo - Prompt Engineering using ChatGPT

Lecture 19 Checkout - Mastering Prompt Engineering for GenAI - Practical Workshop

Section 4: Hands-On Use Cases on GenAI Tools & Platforms

Lecture 20 Overview about Use Cases

Lecture 21 Demo Use Case1 - Email Composition

Lecture 22 Demo Use Case2 - Summarization

Lecture 23 Demo Use Case3 - Pdf Reader | Chat with PDF

Lecture 24 Demo Use Case4 - Content Creation | blogs, youtube video and more

Lecture 25 Demo Use Case5 - Text to Image

Lecture 26 Demo Use Case6 - Text to Video

Lecture 27 Demo Use Case7 - Power Point Presentations

Lecture 28 Demo Use Case8 - Product & Fashion Photo Shoot

Lecture 29 Demo Use Case9 - Design Your Brand logo

Lecture 30 Demo Use Case10 - Capture and Share Insights from Meetings

Lecture 31 Demo Use Case11 - Coding and Development in IT

Lecture 32 Demo Use Case12 - Data Analysis

Lecture 33 Demo Use Case13 - Building Website with GenAI

Lecture 34 Demo Use Case14 - Deploying Website with GenAI

Section 5: Responsible Generative AI

Lecture 35 Potential Risk Involved

Lecture 36 Ethical AI

Lecture 37 AI - Best Practices

Section 6: Live Capstone Project for Practice

Lecture 38 Project Overview

Lecture 39 Amazon Bedrock Overview

Lecture 40 Demo - Amazon Bedrock Service

Lecture 41 Capstone Project | Your own GenAI application

Lecture 42 Demo - Capstone Project | Your own Chatbot using AWS Bedrock Service

Section 7: More Learnings

Lecture 43 More Learnings

AI & ML Engineers,Anyone who wants to get started with AI Ecosystem (AI, ML, DL and Generative AI) Journey,Aspiring AI/Generative AI Enthusiasts,Business Professionals and Leaders, Entrepreneurs, CXOs, Business Managers, CHRO's,Data Scientists/Data Engineers