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Mastering AWS Certified AI Practitioner AIF-C01 – Hands On!

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Includes practice test + exercises! AWS AIF-C01 Certification Prep Course. Bedrock, SageMaker, AI Fundamentals, and more


1. Introduction
  • 1. Introduction
  • 2. Udemy 101
  • 3. Get Your Copy of the Slides.html

  • 2. AI and Machine Learning Fundamentals
  • 1. Taxonomy of Artificial Intelligence and Machine Learning Techniques
  • 2. Supervised Learning Techniques
  • 3. Evaluating Supervised Machine Learning with TrainTest and Cross Validation
  • 4. Unsupervised and Self-Supervised Learning; Reinforcement Learning
  • 5. The Bias Variance Tradeoff
  • 6. A Taxonomy of Machine Learning Techniques
  • 7. Intro to Natural Language Processing (NLP)
  • 8. History of Deep Learning; How Neural Networks Work
  • 9. Convolutional Neural Networks (CNNs)
  • 10. Recurrent Neural Networks (RNNs)
  • 11. The Transformer Architecture and Self-Attention How Generative AI Works
  • 12. Generative Adversarial Networks (GANs)
  • 13. Diffusion Models
  • 14. Quiz AI and ML Fundamentals.html

  • 3. Machine Learning Design Principles and Use Cases
  • 1. Machine Learning Design Principles and Lifecycle
  • 2. Business Goal Identification
  • 3. Framing the Machine Learning Problem
  • 4. Data Processing
  • 5. Model Development, Training, and Tuning
  • 6. Deployment
  • 7. Monitoring
  • 8. The AWS Well-Architected Machine Learning (ML) Lens
  • 9. Machine Learning Ops (MLOps)
  • 10. AI Use Cases
  • 11. Computer Vision Use Cases
  • 12. Generative AI Use Cases
  • 13. Quiz ML Design Principles and Use Cases.html

  • 4. Prompt Engineering
  • 1. Benefits of Prompt Engineering
  • 2. Anatomy of a Prompt
  • 3. Prompt Best Practices
  • 4. Types of Prompts
  • 5. Avoiding Prompt Mis-Use and Mitigating Bias
  • 6. Quiz Prompt Engineering.html

  • 5. AI and Machine Learning with Amazon SageMaker
  • 1. Overview of Amazon SageMaker
  • 2. Data Processing, Training, and Deployment with SageMaker
  • 3. SageMaker Studio, SageMaker Debugger, SageMaker Experiments
  • 4. SageMaker Autopilot
  • 5. SageMaker Model Monitor and SageMaker Clarify
  • 6. SageMaker Deployment Safeguards (and other features)
  • 7. SageMaker Feature Store
  • 8. SageMaker Lineage Tracking
  • 9. SageMaker Data Wrangler
  • 10. DEMO SageMaker Studio, SageMaker Canvas, SageMaker Data Wrangler
  • 11. Linear Learner, XGBoost, Seq2Seq
  • 12. DeepAR, BlazingText, Obj2Vec, Object Detection
  • 13. Image Classification, Semantic Segmentation, Random Cut Forest, NTM, LDA
  • 14. KNN, K-Means, PCA, Factorization Machines, IP Insights
  • 15. DEMO Training and Inference with SageMaker and XGBoost
  • 16. Quiz Amazon SageMaker.html

  • 6. Generative AI with AWS
  • 1. Generative AI with Foundation Models and SageMaker JumpStart
  • 2. Introduction to Amazon Bedrock
  • 3.1 cfas-bylaws-rev-9.docx
  • 3. HANDS ON with the Bedrock Playground for Chat, Text, and Image Generation
  • 4. Fine-Tuning Foundation Models with Bedrock
  • 5.1 book.txt
  • 5. Bedrock Knowledge Bases and Retrieval-Augmented Generation (RAG)
  • 6. HANDS ON with Bedrock Knowledge Bases
  • 7. Bedrock Guardrails
  • 8. HANDS ON with Bedrock Guardrails
  • 9. LLM Agents and Bedrock Agents
  • 10.1 weather.zip
  • 10. HANDS ON with Bedrock Agents
  • 11. More Bedrock Features
  • 12. Amazon Q Developer (formerly CodeWhisperer)
  • 13. Amazon Q Business
  • 14. Amazon Q Apps and Pricing
  • 15. Quiz Generative AI with AWS.html

  • 7. High-Level AWS AI Services
  • 1. Amazon Comprehend
  • 2. Amazon Translate
  • 3. Amazon Transcribe
  • 4. Amazon Polly
  • 5. Amazon Rekognition
  • 6. Amazon Forecast
  • 7. Amazon Lex
  • 8. Amazon Personalize
  • 9. Additional AI and ML Services
  • 10. Quiz High-Level AI Services.html

  • 8. Training, Tuning, and Measuring your Models
  • 1. Fine-Tuning Foundation Models
  • 2. Reinforcement Learning from Human Feedback (RLHF)
  • 3. Preparing Data for Fine Tuning
  • 4. Evaluation Techniques for Foundation Models
  • 5. ROUGE, BLEU, and BERTscore metrics for LLMs
  • 6. Choosing a Generative AI Evaluation Strategy
  • 7. Machine Learning Model Evaluation Precision, Recall, F1, RMSE
  • 8. ROC Curves, AUC, P-R Curves
  • 9. Quiz Training, Tuning, and Measuring your Models.html

  • 9. Responsible AI, AI Governance, and AI Security
  • 1. Dimensions of Responsible AI, and AWS Tools for Responsible AI
  • 2. Best Practices for Responsible AI
  • 3. AI Governance and Service Cards
  • 4. Responsible Model Selection; Responsible Agency
  • 5. Responsible Dataset Preparation
  • 6. Transparency of AI Models
  • 7. AI and Human-Centered Design (HCD)
  • 8. Defense In Depth
  • 9. Additional AI Security Services
  • 10. Security in Data Engineering
  • 11. AWS Shared Responsibility Model
  • 12. AI Compliance Concerns
  • 13. Data Governance
  • 14. AWS AI Governance Services
  • 15. The Generative AI Security Scoping Matrix
  • 16. Quiz Responsible AI, Security, and Governance.html

  • 10. Wrapping Up
  • 1. What to Expect Exam Tips and Preparation
  • 2. Bonus Lecture.html
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    مدت زمان: 550 دقیقه
    تاریخ انتشار: ۲ آبان ۱۴۰۳
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