About this course
In today’s AI/ML landscape—supercharged by cloud computing and big data—Amazon Rekognition offers a fast path from idea to working computer-vision solutions. AWS Rekognition: Machine Learning Using Python Masterclass is a step-by-step, hands-on course that takes you from zero to productive with Rekognition and Python. If you’re new to AWS ML, exploring certifications, or coming from a non-vision background, this course gives you practical skills you can use immediately.
We start with a gentle introduction to Python and the AWS ecosystem, then quickly move into real projects using PyCharm and Boto3 (the AWS SDK for Python). You’ll learn how to detect and extract labels (objects and scenes), text in images (OCR), and faces/face attributes—and how to stitch these capabilities into scripts and simple workflows. Along the way, you’ll follow the flipped-classroom, practice-first approach: short explanations, rich demos, and labs that build confidence without the fluff.
To help you move faster, the course provides reusable code templates, curated sample images, and checklists for setup, troubleshooting, and cost control. We’ll also cover service limits, security basics (IAM roles and permissions), and sensible patterns for storing assets in S3 so your projects scale cleanly.
Note on costs: AWS machine-learning services are not entirely free. While the Free Tier may offset some usage, plan for pay-as-you-go charges during labs. We include guidance to keep costs low.
By the end, you’ll be able to automate common vision tasks, integrate Rekognition into Python applications, and speak confidently about where Rekognition fits in enterprise ML solutions—great preparation for AWS certification journeys and for landing practical, marketable skills.
Learning Objectives
• Set up a safe, low-cost AWS environment (IAM, S3, billing alerts).
• Install and configure PyCharm; manage virtual environments and dependencies.
• Use Boto3 to call Rekognition APIs from Python.
• Detect objects/scenes and extract labels programmatically.
• Perform OCR to extract text from images and post-process results.
• Detect faces and retrieve basic face attributes with confidence scores.
• Handle errors, pagination, throttling, and retries in production-ready code.
• Design simple pipelines that move data between S3 and Rekognition.
• Estimate and manage costs; apply basic security and compliance best practices.
• Map Rekognition skills to AWS certification exam domains at a high level.
Target Audience
• Beginners curious about AWS Rekognition and computer vision.
• Entry-level programmers who want a practical Python + AWS project.
• IT/Cloud professionals upskilling toward AWS certifications.
• Teams evaluating Rekognition for prototypes or internal tooling.
• Note: This course is not intended for intermediate/advanced ML engineers.
Prerequisites
• An AWS account with billing enabled (Free Tier helpful, not required).
• Basic cloud concepts (regions, services, pay-as-you-go).
• Introductory Python (variables, functions, packages) or willingness to learn in-course.
• Ability to install software on your machine (PyCharm) and use the command line.
• Optional but helpful: familiarity with JSON, HTTP APIs, and Git.
We start with a gentle introduction to Python and the AWS ecosystem, then quickly move into real projects using PyCharm and Boto3 (the AWS SDK for Python). You’ll learn how to detect and extract labels (objects and scenes), text in images (OCR), and faces/face attributes—and how to stitch these capabilities into scripts and simple workflows. Along the way, you’ll follow the flipped-classroom, practice-first approach: short explanations, rich demos, and labs that build confidence without the fluff.
To help you move faster, the course provides reusable code templates, curated sample images, and checklists for setup, troubleshooting, and cost control. We’ll also cover service limits, security basics (IAM roles and permissions), and sensible patterns for storing assets in S3 so your projects scale cleanly.
Note on costs: AWS machine-learning services are not entirely free. While the Free Tier may offset some usage, plan for pay-as-you-go charges during labs. We include guidance to keep costs low.
By the end, you’ll be able to automate common vision tasks, integrate Rekognition into Python applications, and speak confidently about where Rekognition fits in enterprise ML solutions—great preparation for AWS certification journeys and for landing practical, marketable skills.
Learning Objectives
• Set up a safe, low-cost AWS environment (IAM, S3, billing alerts).
• Install and configure PyCharm; manage virtual environments and dependencies.
• Use Boto3 to call Rekognition APIs from Python.
• Detect objects/scenes and extract labels programmatically.
• Perform OCR to extract text from images and post-process results.
• Detect faces and retrieve basic face attributes with confidence scores.
• Handle errors, pagination, throttling, and retries in production-ready code.
• Design simple pipelines that move data between S3 and Rekognition.
• Estimate and manage costs; apply basic security and compliance best practices.
• Map Rekognition skills to AWS certification exam domains at a high level.
Target Audience
• Beginners curious about AWS Rekognition and computer vision.
• Entry-level programmers who want a practical Python + AWS project.
• IT/Cloud professionals upskilling toward AWS certifications.
• Teams evaluating Rekognition for prototypes or internal tooling.
• Note: This course is not intended for intermediate/advanced ML engineers.
Prerequisites
• An AWS account with billing enabled (Free Tier helpful, not required).
• Basic cloud concepts (regions, services, pay-as-you-go).
• Introductory Python (variables, functions, packages) or willingness to learn in-course.
• Ability to install software on your machine (PyCharm) and use the command line.
• Optional but helpful: familiarity with JSON, HTTP APIs, and Git.
AWS Rekognition Machine Learning using Python
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AWS Rekognition Machine Learning using Python
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