AWS-ML-Specialty-ELI5-Guide
AWS Machine Learning Specialty Exam Book
A comprehensive guide to prepare for the AWS Machine Learning Specialty Exam, presented in a story-based approach with ELI5 (Explain Like I’m Five) explanations followed by technical deep dives.
📚 Table of Contents
- The Neural Network Story - Deep Learning 101
- The Decision Makers - Activation Functions
- The Power of Teamwork - Ensemble Learning
- The Learning Algorithm - Backpropagation and Gradients
- The Architecture Zoo - Types of Neural Networks
- The Infrastructure Story - AWS Deep Learning Setup
- The Model Zoo - SageMaker Built-in Algorithms
- The Modern Revolution - Transformers and Attention
- The Complete Food Delivery App Case Study
- The Ultimate Reference Guide & Cheat Sheets
🧭 Navigation
- Start Reading: Begin with Chapter 1: The Neural Network Story
- HTML Version: View the complete book in HTML format
- GitHub Pages: Read the book online at https://prabhugr.github.io/AWS-ML-Specialty-ELI5-Guide/
Each chapter includes navigation links to return to this table of contents.
📑 Official AWS Exam Resources
- AWS Certified Machine Learning - Specialty Exam Guide (MLS-C01)
- AWS Machine Learning Specialty Certification Homepage
📊 Exam Content Visual Guide
This visual representation shows the key domains covered in the AWS Machine Learning Specialty exam:
📖 Book Overview
This book covers approximately 90% of the AWS ML Specialty exam content, including:
- Deep learning fundamentals
- Neural network architectures and activation functions
- Ensemble learning techniques
- Backpropagation and gradient optimization
- AWS infrastructure for machine learning
- SageMaker built-in algorithms and implementation
- Modern architectures including Transformers
- Complete case study implementation
- Reference guides and cheat sheets
📋 Chapter Details
Chapter 1: The Neural Network Story - Deep Learning 101
An introduction to neural networks and deep learning fundamentals using simple analogies and ELI5 explanations.
Chapter 2: The Decision Makers - Activation Functions
Detailed exploration of various activation functions, their use cases, advantages, and limitations.
Chapter 3: The Power of Teamwork - Ensemble Learning
Comprehensive coverage of ensemble methods including bagging, boosting, stacking, and random forests.
Chapter 4: The Learning Algorithm - Backpropagation and Gradients
Deep dive into how neural networks learn through backpropagation, gradient descent, and optimization techniques.
Chapter 5: The Architecture Zoo - Types of Neural Networks
Survey of different neural network architectures including CNNs, RNNs, LSTMs, GRUs, and more.
Chapter 6: The Infrastructure Story - AWS Deep Learning Setup
Complete guide to setting up ML infrastructure on AWS, including data engineering with AWS Glue, EMR, Kinesis, and Data Lakes.
Chapter 7: The Model Zoo - SageMaker Built-in Algorithms
Extensive coverage of Amazon SageMaker’s built-in algorithms, their parameters, and use cases.
Chapter 8: The Modern Revolution - Transformers and Attention
Exploration of attention mechanisms and transformer architectures that power modern NLP and computer vision.
Chapter 9: The Complete Food Delivery App Case Study
End-to-end implementation of a machine learning solution, including exploratory data analysis, feature engineering, and deployment.
Chapter 10: The Ultimate Reference Guide & Cheat Sheets
Quick reference guides and cheat sheets for the exam, including AWS AI services like Comprehend, Rekognition, Textract, and Personalize.
📱 HTML Version
A complete HTML version of the book with navigation and styling is available in the html directory.
🔄 Contributing
Feel free to submit issues or pull requests if you find any errors or have suggestions for improvements.
📝 License
This book is provided for educational purposes. All rights reserved.
This content was prepared using Amazon Q for Developer CLI
