What you'll learn

Students will gain a strong foundation knowledge on Machine Learning and Artificial Intelligence.
Students will get lots of hands-on view onto using services on AWS for Machine Learning and Artificial Intelligence
Students will familiarize with services such as Amazon SageMaker, Bedrock and other services related to the field of Machine Learning and AI
Students will gain foundation knowledge when it comes to Generative AI.
Students will be better prepared to attempt the AWS Certified AI Practitioner exam.

Requirements

No prior knowledge is needed on Machine Learning and Artificial Intelligence. We will cover all core concepts in this course.
No prior knowledge is needed on AWS. We will learn in the course itself on how to use the services when it comes to Machine Learning and Artificial Intelligence.

Description

Few words have been spoken more often than 'Generative AI' in today's world. We are witnessing an extraordinary transformation, and it's crucial that we stay prepared and up-to-date with advancements in Artificial Intelligence.The AWS Certified AI Practitioner exam is an excellent starting point. This exam covers the foundational aspects of Machine Learning and AI services offered on AWS, providing a solid foundation for anyone looking to enter the AI field.So what all are we going to cover in this courseFirst and foremost we'll cover the foundational aspects of Machine Learning - We'll learn about the Machine Learning process, how data plays an important role.Then we move into using tools such as Amazon SageMaker Canvas, Data Wrangler to create our Machine Learning model. We'll see how to perform classification and regression from a no-coding aspect.When it comes to Machine Learning, we'll also go through important aspects such as Responsible AI, MLOps, Machine Learning Lifecycle - AWS Well-Architected Framework etc.Then we will move onto learning about the different AWS Managed AI services. This includes the Amazon Comprehend, Amazon Rekognition and other AWS Managed AI services.Then we'll push into learning about Generative AI. We will first have a quick

Overview

on the different foundation models such as OpenAI GPT, Anthropic Claude etc.Next, we'll move onto using Amazon Bedrock on AWS. Will look into using the foundation models available on Amazon Bedrock. Look at the ever important aspect of Prompt Engineering.Next will dive into Security, Governance and Security. We will understand how services like AWS CloudWatch, AWS CloudTrail and many others can supplement the security aspect of our AI-based applications.Finally we have a Practice Test Section - As part of this course, you will have free access to two practice tests. These will allow you to assess your understanding and gauge how well you've grasped the key concepts covered throughout the course.It's the future and its now. Start your path into the world of Artificial Intelligence.

Overview

Section 1: Introduction
Lecture 1 How has the course been structured
Lecture 2 Introduction to Cloud Computing
Lecture 3 Using Amazon Web Services as a cloud service
Lecture 4 Lab - Creating an AWS Account
Lecture 5 Accessing your AWS Account
Lecture 6 Our first AWS service - Amazon S3
Lecture 7 Lab - Working with Amazon S3
Lecture 8 Review of Amazon S3
Section 2: Let's work on Machine Learning
Lecture 9 Understanding different terms
Lecture 10 Considering Machine Learning
Lecture 11 Broad-level understanding of the Machine Learning process
Lecture 12 Data - The star of the show
Lecture 13 Different types of data
Lecture 14 Different types of Machine Learning tasks
Lecture 15 Amazon SageMaker AI
Lecture 16 Quick Intro on different compute options
Lecture 17 Lab - Building an EC2 Instance
Lecture 18 Lab - Connecting to the EC2 Instance
Lecture 19 A note on the costing aspect
Lecture 20 Lab - Creating an Amazon SageMaker domain
Lecture 21 Quick tour of Amazon SageMaker Studio
Lecture 22 Our data set
Lecture 23 Lab - Launching SageMaker Canvas
Lecture 24 Lab - Amazon Canvas - Data Wrangler - Ingesting our data
Lecture 25 Lab - Amazon Canvas - Data Wrangler - Data Insights
Lecture 26 Lab - Amazon Canvas - Data Wrangler - Transforming data
Lecture 27 Lab - Amazon Canvas - Training the Model
Lecture 28 Lab - Amazon Canvas - Making predictions
Lecture 29 Amazon Canvas - Analyzing results
Lecture 30 Amazon SageMaker feature store
Lecture 31 Gotcha's when using training data
Lecture 32 Amazon SageMaker - Using the ready-to-use models
Lecture 33 Amazon SageMaker Jumpstart
Lecture 34 Amazon SageMaker Clarify
Lecture 35 Amazon SageMaker Ground Truth
Lecture 36 Synthetic data
Lecture 37 Different use cases for usage of Machine Learning
Lecture 38 Principles of Response AI
Lecture 39

Overview

on MLOps
Lecture 40 Machine Learning Lifecycle - AWS Well-Architected Framework
Section 3: AWS Managed AI services
Lecture 41 Using the inbuilt AWS AI services
Lecture 42 Amazon Comprehend
Lecture 43 Lab - Using the Amazon Comprehend service
Lecture 44 Amazon Textract
Lecture 45 Lab - Using the Amazon Textract service
Lecture 46 Amazon Transcribe
Lecture 47 Lab - Using Amazon Transcribe
Lecture 48 Amazon Rekognition
Lecture 49 Lab - Using Amazon Rekognition
Lecture 50 Amazon Polly
Lecture 51 Lab - Using Amazon Polly
Lecture 52 Amazon Translate
Lecture 53 Lab - Amazon Translate
Lecture 54 Amazon Forecast
Lecture 55 Amazon Lex
Lecture 56 Lab - Using Amazon Lex
Lecture 57 Amazon Personalize
Lecture 58 Amazon Comprehend Medical
Lecture 59 Amazon Kendra
Section 4: Generative AI
Lecture 60 Large Language Models
Lecture 61 What is a Foundation Model
Lecture 62 Introduction to Generative AI
Lecture 63 A look at using ChatGPT
Lecture 64 Anthropic Claude
Lecture 65 Stable Diffusion
Lecture 66 Hugging Face
Lecture 67 Meta Llama
Lecture 68 What is Amazon Bedrock
Lecture 69 Lab - Amazon Bedrock - Requesting access to models
Lecture 70 Amazon Bedrock - Using Amazon Titan Model
Lecture 71 Amazon Bedrock - Using Amazon Titan Image Generator
Lecture 72 Amazon Bedrock - Inference parameters
Lecture 73 Prompt Engineering
Lecture 74 Prompt Engineering - Be clear
Lecture 75 Prompt Engineering - Different types of prompts
Lecture 76 Prompt Engineering - Using system prompts
Lecture 77 Prompt Engineering - Passing data and instructions
Lecture 78 Prompt Engineering - Prompt Templates
Lecture 79 Prompt Engineering - Resources
Lecture 80 When to choose what model
Lecture 81 Evaluating Foundation Models
Lecture 82 Customizing foundation models
Lecture 83 Amazon Q Developer
Lecture 84 Lab - Amazon RDS Aurora - Launching an instance
Lecture 85 Lab - Amazon RDS Aurora - Connecting to the database
Lecture 86 Lab - Amazon RDS Aurora - Connecting to the database - Resources
Lecture 87 What is Amazon OpenSearch
Lecture 88 What is RAG - Retrieval Augmented Generation
Lecture 89 Amazon Bedrock - Knowledge base - Chat with your document
Lecture 90 Lab - Amazon Bedrock - Knowledge Base - Implementation

Overview

Lecture 91 Lab - Amazon Bedrock - Knowledge Base - Creating an IAM user
Lecture 92 Lab - Amazon Bedrock - Knowledge Base - Implementation
Lecture 93 Challenges on using Generative-AI
Lecture 94 Amazon Bedrock Guardrails
Lecture 95 Lab - Amazon Bedrock Guardrails
Lecture 96 Amazon Bedrock Agents
Lecture 97 More on Amazon Bedrock pricing
Section 5: Security and Monitoring on AWS
Lecture 98 Identity and Access Management
Lecture 99 IAM Users and Groups
Lecture 100 AWS Key Management service and Amazon Bedrock
Lecture 101 What is Amazon CloudWatch
Lecture 102 Amazon Bedrock and Amazon CloudWatch
Lecture 103 Lab - Amazon Bedrock and Amazon CloudWatch
Lecture 104 What is AWS CloudTrail
Lecture 105 Amazon Bedrock - AWS PrivateLink
Lecture 106 Amazon SageMaker and network isolation
Lecture 107 Amazon Macie
Lecture 108 AWS Config
Lecture 109 AWS Artifact
Lecture 110 AWS Audit Manager
Lecture 111 AWS Trusted Advisor
Lecture 112 Quick note on the design of a conversational chatbot
Lecture 113 Securing your Gen-AI applications
Lecture 114 Generative AI Security Scoping Matrix
Section 6: Practice Tests
This course is for students who wants to enter the world of Machine Learning, Artificial Intelligence and Gen-AI. This course will teach students on how to use services on AWS when it comes to Machine Learning, Artificial Intelligence and Gen-AI. This course is meant for students who wants to give the AWS Certified AI Practitioner exam.,This course will teach students on how to use services on AWS when it comes to Machine Learning, Artificial Intelligence and Gen-AI.,This course is meant for students who want to give the AWS Certified AI Practitioner exam.

Homepage:

https://www.udemy.com/course/aws-certified-ai-practitioner-1/