Comprehensive AWS Certified AI Practitioner AIF-C01 Bootcamp

Overview

Master AIF-C01 & Pass on Your First Attempt | 2 Practice Exams + 320 Questions with Detailed Explanations What you'll learn:

Comprehensive Preparation For AWS Certified AI Practitioner (AIF-C01) Certification:

15h High-Quality Video Content + A Total Of 450 Questions & Explanations.[Up-To-Date] Master The AIF-C01 Exam - No Previous Knowledge Needed.[Downloadable] Recap Of Key Concepts - PDF file (119 Pages).Differentiate between Artificial Intelligence, Machine Learning, and Deep Learning.Understand the foundational principles of Neural Networks.Explore the applications of Computer Vision and Natural Language Processing (NLP).Grasp fundamental AWS services and core concepts.Learn the key steps involved in the Machine Learning process.Identify and understand different data types used in Machine Learning.Distinguish and apply the main types of Machine Learning:

Supervised, Unsupervised, Reinforcement, and Semi-Supervised Learning.Understand the concept of Inference in Machine Learning.Explore value-adding applications of Artificial Intelligence.Recognize scenarios where Artificial Intelligence may not be the appropriate solution.Gain practical understanding of Amazon Rekognition for image and video analysis.Learn how to utilize Amazon Transcribe for accurate speech-to-text conversion.Discover the capabilities of Amazon Translate for multilingual text translation.Explore Amazon Comprehend for natural language understanding and insights.Understand how to build conversational interfaces with Amazon Lex.Learn to generate lifelike speech with Amazon Polly.Discover how to leverage Amazon Fraud Detector to identify potential fraud.Explore Amazon Personalize for creating personalized recommendations.Understand how to use Amazon Kendra for intelligent search over documents.Learn to extract text and data from documents with Amazon Textract.Learn how to leverage Amazon Forecast for time-series forecasting.Understand the fundamentals of Amazon Mechanical Turk (MTurk) for crowdsourcing tasks.Explore how to implement human review workflows for machine learning predictions with Amazon Augmented AI (A2I).Gain a comprehensive overview of Amazon SageMaker AI and its key components.Understand the different phases of the Machine Learning Development Lifecycle.Learn the distinction between the ML Development Lifecycle and an ML Pipeline.Grasp the fundamental concepts of MLOps.Discover how AWS SageMaker AI tools map to different stages of the ML Pipeline.Explore model sources and selection strategies within Amazon SageMaker AI.Understand key technical performance metrics for classification problems.Learn essential technical performance metrics for regression problems.Understand the overview and significance of Foundation Models (FMs).Gain insights into the world of Large Language Models (LLMs).Learn about tokens, embeddings, and vectors as fundamental building blocks of language models.Explore the capabilities and applications of Multimodal Models.Discover the principles behind Diffusion Models.Understand the different phases of the Foundation Model Lifecycle.Gain a comprehensive overview of Amazon Bedrock.Understand the purpose and capabilities of Amazon SageMaker JumpStart.Explore Amazon Q Business and Amazon Q Developer for generative AI applications.Understand important inference parameters like Temperature, Top K, Top P, and Output Length.Grasp the concept of Retrieval Augmented Generation (RAG).Explore how to implement RAG and Knowledge Bases using Amazon Bedrock.Understand the different vector database options for storing embeddings.Learn about Foundation Model customization methods, including cost and implementation considerations.Discover how Amazon Bedrock Agents can help accomplish multi-step tasks.Learn fundamental Prompt Engineering techniques to build a strong foundation.Identify and understand various AI vulnerabilities, including exposure, poisoning, hijacking, and prompt injection.Discover various methods for fine-tuning Foundation Models to specific tasks.Understand the crucial steps involved in preparing data for effective Foundation Model fine-tuning.Understand key evaluation metrics for Foundation Models, including Perplexity, BLEU, ROUGE, BERTScore, Accuracy, and F1-Score.Understand the key concepts of Responsible AI.Learn about the legal and ethical concerns surrounding Generative AI.Understand the concepts of Model Fit, Bias, and Variance (Underfitting and Overfitting).Understand AWS AI Service Cards:

what they are, why they are important, and see an example.Explore AWS SageMaker Clarify for detecting and mitigating bias in ML models.Understand AI System Security within the context of the AWS Shared Responsibility Model.Learn about Identity and Access Management (IAM) concepts:

Users, Groups, Roles, Policies, and Permissions.Explore AWS Encryption Capabilities for securing data at rest and in transit.Understand network security considerations for AI workloads, including AWS PrivateLink.Understand the concepts of data provenance and lineage.Discover governance protocols and frameworks specifically designed for Generative AI. Welcome to the Comprehensive AWS Certified AI Practitioner AIF-C01 Bootcamp — your complete guide to passing the exam.My name is Vladimir Raykov, and I’ll be your instructor.

I’m a Certified AI Practitioner, Project Management Professional, Scrum Master, and Product Owner. I currently work as an Agile Product Manager in a software development company.I’ve spent the last 10 years teaching online and have helped thousands of students earn their certifications.

Now, I’m here to help you do the same.By the end of the course, you will:

Be well-prepared to take the official AWS Certified AI Practitioner exam (AIF-C01).Have a strong foundation in core AI, ML, and deep learning concepts — explained simply and clearly - And I’ve created over 300 slides with diagrams and images to make sure that really is the case.Gain a deep understanding of AI-related AWS services like Amazon Bedrock, Amazon SageMaker AI, and pre-trained services such as Comprehend, Rekognition, and many more.Learn how AI is applied in real-world business scenarios and how to evaluate when and how to use AI responsibly.Be ready for the exam’s scenario-based questions by applying what you’ve learned to practical examples throughout the course.As for the structure of the course, you will find:

18 structured sections, aligned with the 5 exam domains:

Fundamentals of AI and ML, Fundamentals of Generative AI, Applications of Foundation Models, Guidelines for Responsible AI, and Security, Compliance, and Governance of AI SolutionsOver 150 bite-sized video lessons (approx. 15 hours total). Every video is scripted to ensure clear, concise delivery — no filler, no “umm” moments320+ practice questions with detailed explanations, included as quizzes at the end of each section2 full-length mock exams, each with 65 questions that mirror the real exam formatA downloadable 119-page PDF summary of key takeaways — perfect for last-minute revisionReal-world AI scenarios to help you connect concepts to practical business use casesRegular updates based on the latest changes in AWS services and exam contentThis course is designed for anyone looking to earn the AWS Certified AI Practitioner (AIF-C01) certification and add it to their professional toolkit — no prior AI or cloud experience required.

Whether you're aiming to understand how AI works in real-world business settings or preparing for your next role, this course will give you the knowledge and confidence to pass the exam.It's perfect for:

Business analysts and IT support professionalsMarketing professionals and product managersProject managers, Product Owners, and Scrum MastersIT managers, sales professionals, and anyone curious about AI and AWSBy the end, you’ll not only be prepared to pass the exam — you'll understand the concepts behind it.Ready to get started? Watch the preview videos—especially ‘Roadmap to Success’—to see my strategy for helping you pass the exam and truly understand the material.Click enroll, and let’s start your AWS AI journey together.

See you inside!

Syllabus

• **Introduction to AI and ML**
• Differentiating AI, ML, and Deep Learning
• Foundational principles of Neural Networks
• Types of Machine Learning: Supervised, Unsupervised, Reinforcement, Semi-Supervised
• Key steps in the Machine Learning process
• Data types in Machine Learning
• Concept of Inference in ML
• Applications and limitations of AI
• **AWS AI Services Overview**
• Core AWS services and concepts
• Amazon Rekognition: Image and video analysis
• Amazon Transcribe: Speech-to-text conversion
• Amazon Translate: Multilingual text translation
• Amazon Comprehend: Natural language understanding
• Amazon Lex and Polly: Conversational interfaces and speech generation
• Amazon Fraud Detector and Personalize
• Amazon Kendra: Intelligent search
• Amazon Textract: Text and data extraction
• Amazon Forecast: Time-series forecasting
• **AWS AI Tools and Frameworks**
• Amazon Mechanical Turk (MTurk) for crowdsourcing
• Amazon Augmented AI (A2I) for human review workflows
• Amazon SageMaker AI: Overview and key components
• Machine Learning Development Lifecycle vs. ML Pipeline
• MLOps fundamentals
• **Generative AI and Foundation Models**
• Large Language Models (LLMs) and Foundation Models
• Tokens, embeddings, and vectors
• Multimodal Models and Diffusion Models
• Phases of Foundation Model Lifecycle
• Amazon Bedrock and SageMaker JumpStart
• Amazon Q Business and Q Developer
• Inference parameters: Temperature, Top K, Top P, Output Length
• **Advanced AI Concepts and Applications**
• Retrieval Augmented Generation (RAG) and Knowledge Bases
• Vector database options for embeddings
• Foundation Model customization methods
• Amazon Bedrock Agents for multi-step tasks
• Prompt Engineering techniques
• AI vulnerabilities and security considerations
• **Responsible AI and Security**
• Responsible AI concepts and ethical concerns
• Model Fit, Bias, and Variance
• AWS AI Service Cards
• SageMaker Clarify: Bias detection and mitigation
• AI System Security within AWS Shared Responsibility Model
• Identity and Access Management (IAM) in AWS
• Encryption and network security for AI workloads
• Data provenance, lineage, and governance protocols
• **Exam Preparation and Practice**
• 150+ bite-sized video lessons
• 320+ practice questions with explanations
• 2 full-length mock exams
• Downloadable 119-page PDF summary
• Regular content updates based on AWS changes
• **Course Conclusion**
• Real-world AI scenarios and business use cases
• Roadmap to Success and exam strategy

Taught by

Vladimir Raykov

Subjects

Programming