Aws Certified Ai Practitioner Aif-C01 Masterclass

AWS Certified AI Practitioner AIF-C01 Masterclass covering Hands-on, quiz and explanations !!

What you'll learn

Gain Knowledge to pass AWS Certified AI Practitioner Exam AIF-C01

Understand AI, ML, and generative AI concepts, methods, and strategies in general and on AWS

Understand the appropriate use of AI/ML and generative AI technologies to ask relevant questions within the candidate’s organization

Determine the correct types of AI/ML technologies to apply to specific use cases.

Use AI, ML, and generative AI technologies responsibly.

Requirements

Individuals who are familiar with, but do not necessarily build, solutions using AI/ML technologies on AWS

Description

Course Description:Unlock the potential of Artificial Intelligence (AI) and Machine Learning (ML) with the AWS Certified AI Practitioner AIF-C01 course. Designed for aspiring AI professionals, this course provides a comprehensive roadmap to mastering AI concepts and preparing for the AWS AI certification exam. Whether you're new to AI or enhancing your existing skills, this course equips you with the knowledge needed to excel in the rapidly evolving AI industry.The course covers five key domains critical to the AWS Certified AI Practitioner exam:Domain 1: Fundamentals of AI and ML (20%) – Understand AI concepts, ML types, and data processing techniques essential for building intelligent solutions.Domain 2: Fundamentals of Generative AI (24%) – Explore generative models, including GPT, and learn how they transform industries through text, image, and code generation.Domain 3: Applications of Foundation Models (28%) – Dive into foundation models like Amazon Bedrock and Hugging Face, gaining insights into real-world applications across various industries.Domain 4: Guidelines for Responsible AI (14%) – Learn AI ethics, fairness, and bias mitigation techniques while ensuring compliance with industry standards.Domain 5: Security, Compliance, and Governance (14%) – Master best practices for securing AI solutions, managing compliance, and adhering to governance policies on AWS.With expert-led tutorials, practical demonstrations, and exam-focused tips, this course ensures you build a solid foundation while preparing confidently for the certification exam. Enroll now to elevate your AI career and become an AWS Certified AI Practitioner!

Overview

Section 1: Domain 1.1 : Explain basic AI concepts and terminologies.

Lecture 1 Introduction to Artificial Intelligence and it's real-life applications

Lecture 2 AI, ML, Deep Learning & Generative AI comparison and examples

Lecture 3 Artificial Intelligence(AI) v/s Machine Learning (ML)

Lecture 4 Typical ML Model building

Lecture 5 Understand Deep Learning with interesting example

Lecture 6 Different types of data in AI

Lecture 7 Define basic AI terms

Lecture 8 Different types of Learning in AI

Lecture 9 Overfitting and Underfitting

Section 2: Domain 1.2: Identify practical use cases for AI

Lecture 10 AI/ML Applications

Lecture 11 ML Types

Lecture 12 AWS Managed AI/ML services - Part 1

Lecture 13 AWS Managed AI/ML services - Part 2

Lecture 14 AWS Managed services Real World examples

Section 3: 1.3: Describe the ML development lifecycle

Lecture 15 Machine Learning Lifecycle and AWS Sagemaker

Lecture 16 Machine Learning Data Processing - Part 1

Lecture 17 Machine Learning Data Processing - Part 2

Lecture 18 Machine Learning training, tunning and evaluating

Lecture 19 Machine Learning model Inference

Lecture 20 Model Monitoring and MLOps

Lecture 21 Model Evaluation Methods - Part 1

Lecture 22 Model Evaluation Methods - Part 2

Section 4: 2.1: Explain concepts of generative AI - Basics

Lecture 23 Generative AI - Power of Gen AI

Lecture 24 Traditional AI v/s Generative AI

Lecture 25 GPT (Generative Pre-Trained Transformer)

Lecture 26 Generative AI Timeline - How Gen AI has evolved and role of Google and AWS

Lecture 27 Generative AI lifecycle

Section 5: 2.2 : Explain concepts of generative AI - Advanced

Lecture 28 What is Token and how to easily calculate tokens using tokenizer

Lecture 29 Understanding Tokens and Temperature

Lecture 30 Embeddings

Lecture 31 Vector Database

Lecture 32 Generative AI Model training - Compare Fine Tuning, RAG and Few shot learning

Lecture 33 Foundational Model

Lecture 34 Foundation models, Gen AI & LLMs

Lecture 35 Multi-modal GenAI

Lecture 36 Transformer Architecture

Lecture 37 Generative AI use cases & Limitations

Section 6: 2.3: Describe AWS infrastructure and technologies for building generative AI app

Lecture 38 AWS Generative AI Layers

Lecture 39 AWS Generative AI services

Lecture 40 Advantages of AWS Gen AI Services

Lecture 41 Amazon Q

Lecture 42 AWS Sagemaker Jumpstart - Opensource Generative AI model hosting in AWS

Section 7: 2.3 : Amazon Bedrock Focus

Lecture 43 Amazon Bedrock - Introduction and Access Hands-on

Lecture 44 Amazon Bedrock - UI options Hands-on

Lecture 45 Prerequisite for Playground - Inference Parameters - Temperature, Top P, Top K

Lecture 46 Amazon Bedrock - Playground - Hands-on Chat, Text and Image Generation

Lecture 47 Amazon Bedrock Guardrail - Hands-on to prevent Prompt attack and block content

Lecture 48 Prerequisite for Knowledge Bases - RAG (Retrieval-augmented generation)

Lecture 49 Amazon Bedrock Knowledge Bases - Hands-on RAG (Retrieval augmented generation)

Lecture 50 AWS Bedrock - Architecture and Design of typical Bedrock application

Section 8: 3.1: Describe design considerations for applications that use foundation models

Lecture 51 Selection Criteria for Pre-Trained Models

Section 9: 3.2: Choose effective prompt engineering techniques

Lecture 52 Introduction to Prompt Engineering

Lecture 53 Few-shot prompting

Lecture 54 Prompt Engineering Tips - Part 1

Lecture 55 Prompt Engineering Tips - Part 2

Lecture 56 Popular Prompt Engineering techniques - Part 1

Lecture 57 Popular Prompt Engineering techniques - Part 2

Lecture 58 AWS Prompt Engineering technique example

Lecture 59 Risks and Limitations in Prompt Engineering

Section 10: 3.3: Describe the training and fine-tuning process for foundation models

Lecture 60 Foundation Models Training

Section 11: 3.4: Describe methods to evaluate foundation model performance.

Lecture 61 Evaluate Foundation Model performance

Section 12: 4.1: Explain the development of AI systems that are responsible

Lecture 62 Responsible & Ethical AI

Lecture 63 Risks and mitigation of Gen AI

Section 13: 4.2: Recognize the importance of transparent and explainable models

Lecture 64 Transparency in AI

Section 14: 5.1: Explain methods to secure AI systems

Lecture 65 AWS Shared responsibility and IAM introduction

Lecture 66 AWS IAM Policies, Users, Roles, Groups and Identity Center Hands on

Lecture 67 IAM features and new account creation

Section 15: 5.2: Recognize governance and compliance regulations for AI systems

Lecture 68 Governance and compliance in AWS

Lecture 69 AWS tools - Audit Manager, Config, Amazon Inspector & Trusted Advisor Hands on

Section 16: AWS services Basics

Lecture 70 AWS S3

Lecture 71 AWS Lambda

Lecture 72 AWS API Gateway

Anybody who has interest in AI and AWS