Mastering Ai On Aws: Training Aws Certified Ai Practitioner

Building AI and Machine Learning Solutions with AWS Services: From Fundamentals to Certification Success

What you'll learn

Understand Key Concepts of AI and Machine Learning on AWS

Master AWS AI and Machine Learning Services

Build and Deploy AI-Powered Applications on AWS

Prepare for the AWS Certified AI Practitioner Exam

Requirements

Basic Knowledge of Cloud Computing: Students should have a general understanding of cloud computing concepts and experience using AWS services, such as EC2, S3, or RDS

Familiarity with Programming: A basic understanding of programming languages, especially Python, is recommended, as some hands-on labs will involve coding for machine learning and AI tasks.

Understanding of Machine Learning Fundamentals (Optional but Beneficial): While the course will cover the basics of machine learning, having prior knowledge of key ML concepts (like algorithms, training, and model evaluation) will be helpful.

AWS Account: Students will need an active AWS account to perform hands-on labs and practice with AWS AI and ML services.

Description

This comprehensive course, "Mastering AI on AWS: Training AWS Certified AI Practitioner" is designed to equip you with the knowledge and skills to excel in AI and machine learning using AWS services. Whether you're a cloud professional, developer, or AI enthusiast, this course will guide you through the fundamentals of AI and machine learning while providing hands-on experience with cutting-edge AWS AI services like Amazon SageMaker, Rekognition, Comprehend, Polly, and more.Starting with foundational concepts of AI and machine learning, you’ll progress through practical labs, working with real-world applications such as image and video recognition, natural language processing, and recommendation systems. The course will also cover security best practices, responsible AI, and preparing for the AWS Certified AI Practitioner exam. By the end, you’ll be ready to build, deploy, and monitor AI applications on AWS and confidently pass the certification exam.Through engaging lessons, hands-on projects, and practical exercises, this course ensures you develop both theoretical knowledge and practical skills to succeed in the growing field of AI and machine learning.What you'll learn:Fundamental concepts of AI, machine learning, and AWS AI services.How to build and deploy AI applications using Amazon SageMaker, Rekognition, Comprehend, Polly, and more.Best practices for securing AI and machine learning workflows on AWS.How to prepare for and pass the AWS Certified AI Practitioner exam.  Who this course is for:Cloud professionals wanting to expand into AI/ML.AI/ML enthusiasts looking to gain practical skills using AWS services.Aspiring data scientists and developers seeking to implement real-world AI solutions.Students and professionals preparing for the AWS Certified AI Practitioner exam.

Overview

Section 1: Introduction to AWS AI and Machine Learning

Lecture 1 What will we Cover

Lecture 2 Overview of AWS AI and ML Services

Lecture 3 Importance of AI in Cloud Computing

Lecture 4 Introduction to AWS Certified AI Practitioner Exam

Lecture 5 Key Concepts: AI, Machine Learning, and Deep Learning

Lecture 6 Prerequisites and Exam Preparation Strategy

Section 2: Fundamentals of Machine Learning (ML)

Lecture 7 What will we cover

Lecture 8 Supervised vs Unsupervised Learning

Lecture 9 Key Machine Learning Algorithms

Lecture 10 Training vs Inference in Machine Learning

Lecture 11 Introduction to Model Evaluation and Performance

Lecture 12 Hands-On Lab: Training a Simple Machine Learning Model

Section 3: AWS AI Services Overview

Lecture 13 What will we cover

Lecture 14 Amazon SageMaker Overview

Lecture 15 AWS AI Services for Vision, Speech, Language, and Recommendations

Lecture 16 Introduction to AWS AI Service Use Cases

Lecture 17 AI and ML Decision-Making Process on AWS

Lecture 18 Hands-On Lab: Exploring AWS AI Services

Section 4: AWS AI Services for Natural Language Processing (NLP)

Lecture 19 What will we cover

Lecture 20 Overview of NLP and its Applications

Lecture 21 Amazon Comprehend: Sentiment Analysis, Entity Recognition, & Language Detection

Lecture 22 Amazon Transcribe: Speech-to-Text Transcription

Lecture 23 Amazon Translate: Real-Time Language Translation

Lecture 24 Hands-On Lab: Analyzing Text Data with Amazon Comprehend

Section 5: AWS AI Services for Computer Vision

Lecture 25 What will we cover

Lecture 26 Introduction to Computer Vision on AWS

Lecture 27 Amazon Rekognition: Image and Video Analysis

Lecture 28 Amazon Textract: Extracting Text from Documents

Lecture 29 Hands-On Lab: Image and Video Processing with Amazon Rekognition

Section 6: AWS AI Services for Speech Recognition

Lecture 30 What will we cover

Lecture 31 Amazon Polly: Text-to-Speech Conversion

Lecture 32 Amazon Transcribe: Automatic Speech Recognition

Lecture 33 Building Real-Time Speech Interfaces on AWS

Lecture 34 Hands-On Lab: Building a Voice Interface with Amazon Polly

Section 7: AI and Machine Learning Security on AWS

Lecture 35 What will we cover

Lecture 36 Security and Compliance in AWS AI Services

Lecture 37 Data Encryption and Security in Machine Learning Workflows

Lecture 38 Monitoring and Logging in SageMaker and AWS AI Services

Lecture 39 Hands-On Lab: Implementing Security Best Practices for AI Services

Section 8: AWS AI Services for Personalization and Recommendations

Lecture 40 What will we cover

Lecture 41 Introduction to Amazon Personalize

Lecture 42 Building Recommendation Engines

Lecture 43 Use Cases: E-commerce, Media, and Healthcare

Lecture 44 Hands-On Lab: Creating a Personalized Recommendation System

Section 9: AI and ML Use Cases on AWS

Lecture 45 What will we cover

Lecture 46 AI in Healthcare, Finance, Retail, and Manufacturing

Lecture 47 Real-World Examples of AWS AI Services in Production

Lecture 48 Case Studies: Successful AI and ML Projects on AWS

Lecture 49 Group Discussion: Best Practices for AI Deployment

Section 10: AI Ethics and Responsible AI on AWS

Lecture 50 What will we cover

Lecture 51 Importance of Ethics in AI

Lecture 52 AWS Guidelines for Responsible AI Use

Lecture 53 Fairness, Bias, and Interpretability in AI Models

Lecture 54 Hands-On Lab: Ensuring Fairness and Mitigating Bias in AI Models

Section 11: Exam Preparation and Mock Tests

Lecture 55 What will we cover here

Lecture 56 AWS Certified AI Practitioner Exam Structure and Scoring

Lecture 57 Key Exam Topics and Concepts to Focus On

Lecture 58 Practice Exam Questions and Sample Tests

Lecture 59 Time Management and Exam Day Strategies

Lecture 60 Final Exam Preparation Checklist

Cloud Professionals Seeking to Expand into AI/ML: Cloud engineers, architects, and developers who are familiar with AWS and want to build a foundation in AI and machine learning technologies using AWS services.,AI and Machine Learning Enthusiasts: Individuals interested in learning the fundamentals of AI and machine learning, and how to apply these technologies in real-world scenarios using AWS.,Aspiring Data Scientists and ML Engineers: Beginner data scientists and machine learning engineers looking to gain hands-on experience with AWS AI services, such as Amazon SageMaker, and learn how to build, deploy, and manage AI models on the cloud.,Business Analysts and Decision Makers: Professionals who want to understand the capabilities of AI and ML on AWS in order to make informed decisions, manage AI projects, and leverage AI technologies to solve business problems.,Students Preparing for AWS AI Certification: Anyone preparing for the AWS Certified AI Practitioner exam who needs structured learning materials, practice labs, and exam preparation resources to ensure success.,Tech Professionals Looking to Upskill: IT professionals, developers, and cloud practitioners who want to enhance their career prospects by gaining AI and machine learning skills in a cloud environment.