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AWS Certified AI Practitioner Study Notes

AI & Machine Learning Fundamentals

Foundation concepts for AI and ML

AI vs ML vs Deep Learning

Concept	Definition	Example
Artificial Intelligence	Machines mimicking human intelligence	Virtual assistants, game AI
Machine Learning	Learning from data without explicit programming	Spam filters, recommendations
Deep Learning	Neural networks with multiple layers	Image recognition, NLP
Generative AI	Creating new content from patterns	ChatGPT, DALL-E, Stable Diffusion

Types of Machine Learning

Supervised Learning

Labeled data: classification, regression (predict house prices, spam detection)

Unsupervised Learning

Unlabeled data: clustering, anomaly detection (customer segments)

Reinforcement Learning

Agent learns through rewards/penalties (game playing, robotics)

Self-supervised

Model creates own labels from data (foundation models, LLMs)

Common ML Tasks

Classification: Categorize into discrete classes (spam/not spam)
Regression: Predict continuous values (price, temperature)
Clustering: Group similar items (customer segments)
Recommendation: Suggest relevant items (products, content)

Exam Focus Areas

Supervised = labeled data, Unsupervised = unlabeled data
Classification = categories, Regression = numbers
Deep learning excels with large datasets and unstructured data
Foundation models are pre-trained on massive datasets

ML Development Lifecycle

Steps in building ML solutions

ML Pipeline Stages

Stage	Activities	AWS Service
Business Problem	Define objectives, success metrics	—
Data Collection	Gather, aggregate data sources	S3, Glue, Kinesis
Data Preparation	Clean, transform, feature engineering	Glue DataBrew, SageMaker
Model Training	Algorithm selection, hyperparameter tuning	SageMaker
Evaluation	Metrics, validation, bias detection	SageMaker Clarify
Deployment	Inference endpoints, monitoring	SageMaker Endpoints

Key ML Metrics

Accuracy

Correct predictions / Total predictions

Precision

True positives / Predicted positives

Recall

True positives / Actual positives

F1 Score

Harmonic mean of precision and recall

Overfitting vs Underfitting

Overfitting: Model memorizes training data, poor generalization

Underfitting: Model too simple, misses patterns

Solution: Cross-validation, regularization, more/better data

Exam Focus Areas

Data quality is critical - garbage in, garbage out
Feature engineering often has more impact than algorithm choice
Always split data: training, validation, test sets
Monitor model drift in production

AWS Certified AI Practitioner Study Notes

AI & Machine Learning Fundamentals

Foundation concepts for AI and ML

AI vs ML vs Deep Learning

Concept	Definition	Example
Artificial Intelligence	Machines mimicking human intelligence	Virtual assistants, game AI
Machine Learning	Learning from data without explicit programming	Spam filters, recommendations
Deep Learning	Neural networks with multiple layers	Image recognition, NLP
Generative AI	Creating new content from patterns	ChatGPT, DALL-E, Stable Diffusion

Types of Machine Learning

Supervised Learning

Labeled data: classification, regression (predict house prices, spam detection)

Unsupervised Learning

Unlabeled data: clustering, anomaly detection (customer segments)

Reinforcement Learning

Agent learns through rewards/penalties (game playing, robotics)

Self-supervised

Model creates own labels from data (foundation models, LLMs)

Common ML Tasks

Classification: Categorize into discrete classes (spam/not spam)
Regression: Predict continuous values (price, temperature)
Clustering: Group similar items (customer segments)
Recommendation: Suggest relevant items (products, content)

Exam Focus Areas

Supervised = labeled data, Unsupervised = unlabeled data
Classification = categories, Regression = numbers
Deep learning excels with large datasets and unstructured data
Foundation models are pre-trained on massive datasets

ML Development Lifecycle

Steps in building ML solutions

ML Pipeline Stages

Stage	Activities	AWS Service
Business Problem	Define objectives, success metrics	—
Data Collection	Gather, aggregate data sources	S3, Glue, Kinesis
Data Preparation	Clean, transform, feature engineering	Glue DataBrew, SageMaker
Model Training	Algorithm selection, hyperparameter tuning	SageMaker
Evaluation	Metrics, validation, bias detection	SageMaker Clarify
Deployment	Inference endpoints, monitoring	SageMaker Endpoints

Key ML Metrics

Accuracy

Correct predictions / Total predictions

Precision

True positives / Predicted positives

Recall

True positives / Actual positives

F1 Score

Harmonic mean of precision and recall

Overfitting vs Underfitting

Overfitting: Model memorizes training data, poor generalization

Underfitting: Model too simple, misses patterns

Solution: Cross-validation, regularization, more/better data

Exam Focus Areas

Data quality is critical - garbage in, garbage out
Feature engineering often has more impact than algorithm choice
Always split data: training, validation, test sets
Monitor model drift in production