Discover supervised learning with labeled data that maps inputs to outputs, and unsupervised learning with unlabeled data that uncovers patterns such as clustering and principal component analysis.

Explore key supervised and unsupervised machine learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, k means clustering, hierarchical clustering, and PCA, with use cases.

Understand training and inference in machine learning, including supervised learning, labels, input features, and gradient descent optimization across epochs to generalize to unseen data, exemplified by AWS SageMaker workflows.

Explore evaluating machine learning models on AWS using metrics like accuracy, precision, recall, F1, and MSE. Understand confusion matrices, overfitting, underfitting, and cross-validation for deployment readiness.

Explore AWS AI services for vision, speech, language, and recommendations, and how they support machine learning decision making. Gain hands-on experience with Amazon SageMaker through an AWS AI services lab.

Discover real-world AWS AI services use cases across industries, from Rekognition image search and content moderation to Personalize recommendations and SageMaker predictive maintenance.

Learn the AI and ML decision-making workflow on AWS, from problem definition and data prep to training, deployment, monitoring, and optimization with SageMaker, Rekognition, and Comprehend.

Leverage amazon transcribe to convert speech to text with real-time and batch transcription, speaker identification, custom vocabulary, and timestamps, and integrate with Polly for speech synthesis.

Learn how Amazon Rekognition enables image and video analysis with object and facial detection, text extraction, content moderation, and real time processing via S3, Kinesis, and API integration.

Explore image and video analysis with Amazon Rekognition to detect objects, faces, and text, and demonstrate end-to-end lab steps from setup to result review.

Explore AWS AI services for speech recognition, including Amazon Polly for text-to-speech and Amazon Transcribe for automatic speech recognition, and build real-time speech interfaces.

Master Amazon Polly, a cloud-based text-to-speech service using deep learning to convert text into natural speech across multiple languages and voices, with support for timestamps and lexicons.

Explore Amazon Transcribe, a fully managed automatic speech recognition service that converts speech to text, with real-time and batch transcription, speaker identification, and time stamping for diverse applications.

Build a simple voice interface using Amazon Polly to convert text to speech, synthesize speech, download audio, and customize voice settings, while exploring transcription with Amazon Transcribe and Lambda integration.

Secure AI and ML workloads on AWS by applying encryption, compliance, monitoring, and logging across AWS AI services and SageMaker, with a hands-on lab implementing security best practices.

Encrypt training data and models in encrypted S3 buckets, secure SageMaker notebooks in a VPC, and monitor activity with CloudWatch and CloudTrail for AI services.

Explore Amazon Personalize for building personalized recommendation engines, examine real-world use cases across e-commerce, media, and healthcare, and complete a hands-on lab creating a personalized recommendation system.

Explore use cases for e-commerce, media, and healthcare with Amazon Personalize, delivering personalized product recommendations, dynamic search results, and content recommendations for media and healthcare.

Explore real-world production uses of AWS AI services across industries, from Netflix personalizing with Amazon Personalize to Intuit Textract, Moderna with Amazon SageMaker, Zalando Rekognition, and Capital One fraud detection.

Explore how AWS case studies reveal successful AI and ML projects, from F1 real-time insights with SageMaker, Lambda, and S3 to underwriting automation with Textract.

Explore fairness, bias, and interpretability in AI models, addressing data, algorithmic, and representation biases with tools like SageMaker Clarify, Shap, and Lime for transparent AI.