Mastering Semantic Kernel By Creating Projects

Learn to harness the potential of Semantic Kernel and build advanced AI applications using OpenAI and Azure OpenAI.

What you'll learn

Fundamentals of Semantic Kernel

Kernel Creation

Generating Images, Text, Audio, and Transcriptions Using AI

Using Chat Histories

Using and Creating Native Plugins

Prompting Techniques

Using Vector Stores

Textual Search

Requirements

Basic programming knowledge (ideally in C# or .NET)

Willingness to learn and experiment with Semantic Kernel and AI plugins

OpenAI account or access to Azure OpenAI services

Description

Do you want to integrate artificial intelligence into your applications efficiently and effectively? This course is your gateway to the world of Semantic Kernel, a powerful Microsoft tool that enables you to enhance your developments with language models (LLMs) like OpenAI and Azure OpenAI.What will you learn in this course?VectorStores and Semantic Search: Learn how to use embeddings to store and retrieve information efficiently, reducing token consumption and optimizing queries.Integration with OpenAI and Azure OpenAI Models: Generate embeddings, process text, and perform vector searches using technologies like TextEmbeddingADA002.Retrieval-Augmented Generation (RAG): Improve AI model accuracy by combining web searches and vector databases with Semantic Kernel.Process Automation with Plugins: Implement custom plugins in C# to connect external APIs and perform specialized tasks.Application Development with Semantic Kernel: Build everything from interactive chatbots to automated content generators for WordPress and podcasts.Integration with FFmpeg: Extract audio from videos, transcribe content with Whisper, and generate clips for social media automatically.Advanced Prompt Engineering and Templates: Learn how to structure effective prompts using YAML, Handlebars, and Liquid to optimize AI interactions.Who is this course for?Developers looking to implement AI in their applications using .NET and C#Data scientists and NLP specialists who want to enhance their models with vector searchesContent creators and automation enthusiasts interested in generating text, audio, and images with AIProfessionals seeking to master advanced Semantic Kernel techniques and its integration with OpenAI and AzureWhy take this course?100% hands-on: Real-world projects from installation to final implementationCutting-edge technology: Learn to leverage Semantic Kernel, a key SDK for developing AI copilots and intelligent assistantsPractical use cases: From intelligent chatbots to automated WordPress posts and AI-generated podcastsSupport and community: Access an active community and updated materials featuring the latest AI toolsIf you want to take AI to the next level and integrate it into real-world projects, this course is for you.Enroll now and become an expert in Semantic Kernel and applied AI!

Overview

Section 1: Introducción

Lecture 1 What is Semantic Kernel?

Lecture 2 Semantic Kernel Components

Lecture 3 Benefits and Use Cases

Lecture 4 Setting Up a VS 2022 Project with Semantic Kernel

Section 2: The Kernel - Core Engine

Lecture 5 Understanding Kernel as orchestrator

Lecture 6 The Builder pattern

Lecture 7 Builder Pattern Demo

Lecture 8 Creating an API Key to connect to OpenAI

Lecture 9 Creating an API Key to connect to Azure OpenAI

Lecture 10 Creating the project, environment variables and Kernels

Lecture 11 Chat Completion using Semantic Kernel

Lecture 12 Chat Completion Streaming using Semantic Kernel

Lecture 13 Generating images using Semantic Kernel

Lecture 14 Generating Audio Files using Semantic Kernel

Lecture 15 Extracting Text from Audio using Semantic Kernel

Section 3: Workshop - Creating Blog Posts using Semantic Kernel

Lecture 16 About the project

Lecture 17 Creating the project and configuring the Kernels

Lecture 18 Generating the Blog Post

Lecture 19 Generating a Featured Image for the blog post

Lecture 20 Generating the Blog Post Audio File

Lecture 21 Publishing the content on Wordpress

Section 4: Getting Started with Chat Completion

Lecture 22 Using the Chat Completion Service

Lecture 23 Adding Chat History

Lecture 24 Multi-modal chat completion

Section 5: Workshop - Creating a Multi-modal Chat Application

Lecture 25 Introduction to the Section

Lecture 26 Creating and setting up the project

Lecture 27 Displaying chat instructions

Lecture 28 Interacting with the chat service

Lecture 29 Adding the ability to read images

Section 6: Plugins

Lecture 30 The foundation of plugins in Semantic Kernel

Lecture 31 Creating Kernel Functions

Lecture 32 Creating Native Plugins

Lecture 33 Creating your first native plugins

Lecture 34 Using built-in plugins

Lecture 35 Function Calling

Lecture 36 Function Calling in Semantic Kernel

Lecture 37 Function Calling in Action

Lecture 38 AddFromObject vs AddFromType

Lecture 39 Function Choice Behavior

Lecture 40 Function Invocation

Lecture 41 Dealing with complete objects as parameters

Lecture 42 Adding OpenAPI plugins

Section 7: Workshop - Create a Video Insights app

Lecture 43 Introduction to the Section

Lecture 44 Creating and configuring the initial project

Lecture 45 Installing ffmpeg

Lecture 46 Extracting the audio file from a video file

Lecture 47 Compressing the audio File for transcription

Lecture 48 Implementing Speech to Text to get the transcription

Lecture 49 Cutting video highlights

Lecture 50 Burning subtitles

Section 8: Prompts

Lecture 51 Prompting fundamentals

Lecture 52 Using Semantic Kernel prompt templates

Lecture 53 Converting prompts to ChatHistory instances

Lecture 54 Using variables in prompt templates

Lecture 55 Handlebars Prompt Templates

Lecture 56 Liquid Prompt Templates

Lecture 57 Separating prompt templates into YAML files

Section 9: Workshop - Create a Podcast Generator App

Lecture 58 Introduction to the Section

Lecture 59 Configuring the project

Lecture 60 Input Data Collection

Lecture 61 Markdown Conversion

Lecture 62 Generating the first draft

Lecture 63 Generating the Podcast Script

Lecture 64 Generating the Conclusion

Lecture 65 Podcast Generation

Section 10: Memory (Vector Stores) and Text Search

Lecture 66 What are Embeddings and Vector Stores?

Lecture 67 Defining your Data Model

Lecture 68 Generating embeddings and saving in Vector Stores

Lecture 69 Performing Vector Search

Lecture 70 Text Search

Lecture 71 Text Search Plugins

Lecture 72 Text Search Plugins - Function Calling

Lecture 73 Text Search with Vector Stores

Students and professionals who want to expand their toolkit for building intelligent applications with natural language processing.,Entrepreneurs and AI solution creators interested in leveraging AI service capabilities.,Developers who want to integrate AI into their applications.,Anyone interested in building applications with advanced AI capabilities.