[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-317515-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-317515-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","aws-certified-ai-practitioner-aif-c01-course-duration-16-hours-2-days","AWS Certified AI Practitioner (AIF-C01) - Course Duration 16 hours - 2 Days","","AWS Certified AI Practitioner (AIF-C01) course provides beginner-to-professional training on building foundational AI and ML knowledge on AWS. The program covers core AI/ML concepts, AWS AI service capabilities, and practical workflows for real-world AI-driven solutions. Learners practice with pre-trained services such as Amazon Comprehend, Rekognition, Textract, Polly, and Transcribe, then explore the ML model lifecycle using Amazon SageMaker. It also includes prompt engineering, Amazon Q, responsible AI and ethics, AWS security services, and guidance for ALF-C01 exam preparation and next 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Practitioner course is designed for beginners and professionals looking to gain foundational knowledge in Artificial Intelligence (AI) and Machine Learning (ML) on AWS. This course covers key AI/ML concepts, AWS AI services, and practical applications of AI-driven solutions. Participants will learn about machine learning workflows, AWS AI services like Amazon SageMaker, AWS AI/ML ethics, and responsible AI practices.  \nCourse objectives  \n• By the end of this course, participants will be able to:  \n• Understand the fundamentals ofAI and ML, including key concepts, techniques, and terminologies.  \n• Recognize the AWS AI/ML service landscape and their applications in real-world scenarios.  \n• Work with pre-trained AI services such as Amazon Comprehend, Rekognition, Textract, Polly, and Transcribe.  \n• Understand the ML model lifecycle and basic concepts of supervised, unsupervised, and reinforcement learning.  \n• Use Amazon SageMaker for building, training, and deploying ML models.  \n• Explore ethical AI principles and responsible AI practices in AWS.  \nPrerequisites  \n• Basic understanding of cloud computing and AWS fundamentals.  \n• No prior AI/ML experience required, but familiarity with basic statistics and data concepts is beneficial.  \nTarget Audience  \n• Beginners & Business Professionals looking to understand AI/ML on AWS.  \n• Developers & Cloud Engineers seeking to integrate AWS AI services into applications.  \n• Data Analysts & Scientists exploring AWS ML tools and automation.  \n• IT Leaders & Decision Makers assessing AI/ML adoption strategies.  \n• Students & AI Enthusiasts preparing for AWS AI certification.  \nCourse outline  \nModule 1: Introduction to Cloud Computing  \n• What is Cloud Computing  \n• Traditional Data Center  \n• Different types of Cloud Computing  \n• AWS Cloud Overview  \n• Shared responsibility model  \nModule 2: Introduction to AI and Machine Learning on AWS  \n• Overview of AI/ML concepts  \n• AWS AI and ML services overview  \n• Use cases of AI/ML in real-world scenarios  \nModule 3: Machine Learning Fundamentals  \n• Supervised, Unsupervised, and Reinforcement Learning  \n• Key ML concepts: Features, Models, and Algorithms  \n• Steps in a machine learning pipeline  \nModule 4: AWS AI Services for Text & Speech Processing  \n• Introduction to Amazon Comprehend for NLP.  \n• Text-to-speech with Amazon Polly.  \n• Speech-to-text with Amazon Transcribe.  \nHands-on Lab: Converting text to speech and extracting key phrases from text.  \nModule 5: AWS AI Services for Image & Video Processing  \n• Introduction to Amazon Rekognition for image and facial analysis.  \n• Video content analysis with Rekognition Video.  \n• Document processing with Amazon Textract.  \nHands-on Lab: Analyzing images and extracting text from documents.  \nModule 6: Machine Learning with Amazon SageMaker  \n• Introduction to machine learning models.  \n• Supervised vs. Unsupervised learning.  \n• Building, training, and deploying ML models using Amazon SageMaker. Module 7: Prompt Engineering  \n• What is prompt engineering  \n• Prompt performance optimization  \n• Prompt Engineering Techniques  \n• Prompt templates  \nModule 8: Amazon Q  \n• Introduction  \n• Amazon Q business  \n• Amazon Q Apps  \n• Amazon Q developer  \n• AWS PartyRock  \nModule 9: Ethics, Responsible AI, and AWS AI/ML Best Practices  \n• Understanding AI bias and fairness.  \n• AWS responsible AI principles.  \n• Governance and security best practices for AI applications.  \nModule 10: AWS Security Services  \n• IAM  \no Users, Groups, Policies  \no Roles  \n• Amazon EC2  \no Create an EC2 instance with User data to have a website  \n• AWS Lambda  \n• Amazon Macie  \n• Amazon Inspector  \n• AWS Config  \n• AWS Cloudtrail  \n• AWS Artifact  \n• AWS Trusted Advisor  \nModule 11: Exam Preparation and Next Steps  \n• Understanding the ALF-C01 exam format and domains  \n• Sample questions and key concepts review  \n• Ti","cbCaiouWfHJsNNy2","https://ap.wps.com/l/cbCaiouWfHJsNNy2","pdf",75145,"English","# Course Objectives\n## Prerequisites and Target Audience\n# Course Outline\n## Module 1-3: Cloud Computing and AI/ML Fundamentals\n## Module 4-5: Text/Speech and Image/Video Processing\n## Module 6-8: SageMaker and Prompt Engineering\n## Module 9-10: Ethics/Responsible AI and AWS Security Services\n## Module 11: Exam Preparation and Next Steps","[{\"question\":\"Who should attend the AWS Certified AI Practitioner (AIF-C01) course?\",\"answer\":\"The course targets beginners and business professionals, developers and cloud engineers, data analysts and scientists, IT leaders and decision makers, and students preparing for AWS AI certification.\"},{\"question\":\"What AWS AI services are covered in the course?\",\"answer\":\"Participants will work with pre-trained AI services including Amazon Comprehend, Rekognition, Textract, Polly, and Transcribe, and later learn machine learning model development with Amazon SageMaker.\"},{\"question\":\"What skills are developed by the end of the course?\",\"answer\":\"Learners can understand AI and ML fundamentals, recognize the AWS AI/ML service landscape, apply pre-trained services, grasp the supervised/unsupervised/reinforcement learning concepts, and use Amazon SageMaker for building, training, and deploying ML models.\"}]","AWS Certified AI Practitioner (AIF-C01) - Course Duration 16 hours - 2 Days | PDF",1789934468]