Generative Ai Essentials - Practical Use Cases
Generative Ai Essentials - Practical Use Cases
Published 6/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.45 GB | Duration: 4h 1m
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
![Generative Ai Essentials - Practical Use Cases]()
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
