1. Amazon AI Services: Computer Vision
1.1. Introduction to Amazon Rekognition
1.2. Introduction to AWS DeepLens
1.3. Hands-on Rekognition: Automated Video Editing
1.4. Deep Dive on Amazon Rekognition
2. Amazon AI Services: NLP
2.1. Machine Learning Pipeline
2.1.1. Introduction to Amazon Comprehend
2.1.2. Introduction to Amazon Comprehend Medical
2.1.3. Introduction to Amazon Translate
2.1.4. Introduction to Amazon Transcribe
2.1.5. Deep Dive with Amazon Transcribe
3. Introduction to Amazon SageMaker
3.1. Introduction to Amazon SageMaker
3.2. Introduction to Amazon SageMaker GroundTruth
3.3. Introduction to Amazon SageMaker Neo
3.4. Automatic model tuning using Amazon SageMaker
3.5. Amazon Sagemaker: Object Detection on Images labeled with Ground Truth
3.6. Build a text classification model with Glue and Sagemaker
4. Introduction to Machine Learning
4.1. What is Artificial Intelligence?
4.1.1. WHAT IS AI ?
4.1.2. WHY IS AI IMPORTANT ?
4.1.3. WHAT IS ML AND DL ?
4.1.3.1. WHAT CAN ML DO ?
4.1.3.1.1. MAKE PREDICTIONS
4.1.3.1.2. OPTIMIZE UTILTY FUNCTIONS
4.1.3.1.3. EXTRACT HIDDEN DATA STRUCTURES
4.1.3.1.4. CLASSIFY DATA
4.1.4. HOW AMAZON USES AI IN ITS PRODUCTS ?
4.1.4.1. DISCOVERY AND SEARCH
4.1.4.2. FULFILLMENT & LOGISTICS
4.1.4.3. ENHANCING EXISTING PRODUCTS
4.1.4.4. DEFINING NEW PRODUCT CATEGORIES
4.1.4.5. BRINGING ML TO ALL
4.1.5. AI SUPPORTING SERVICES AND FRAMEWORKS AVAILABLE IN AWS
4.1.5.1. SERVICES
4.1.5.1.1. VISION
4.1.5.1.2. SPEECH
4.1.5.1.3. CHAT
4.1.5.2. PLATFORMS
4.1.5.2.1. AMAZON ML
4.1.5.2.2. SPARK & EMR
4.1.5.2.3. KINESIS
4.1.5.2.4. BATCH
4.1.5.2.5. ECS
4.1.5.3. ENGINES
4.1.5.3.1. MXNET
4.1.5.3.2. TENSORFLOW
4.1.5.3.3. CAFFE
4.1.5.3.4. THEANO
4.1.5.3.5. PYTORCH
4.1.5.3.6. CNTK
4.1.5.4. INFRASTRUCTURE
4.1.5.4.1. CPU
4.1.5.4.2. GPU
4.1.5.4.3. IOT
4.1.5.4.4. MOBILE
4.1.6. USE CASES
4.1.6.1. MEDIA & ENTERTAINMENT
4.1.6.2. PUBLIC SAFETY
4.1.6.3. HEALTH CARE
4.1.6.4. LAW ENFORCEMENT
4.1.6.5. DIGITAL ASSET MANAGEMENT
4.1.6.6. INFLUENCER MARKETING
4.1.6.7. DIGITAL ADVERTISING
4.1.6.8. EDUCATION
4.1.6.9. CONSUMER STORAGE
4.1.6.10. GEO-LOCATION SERVICES
4.1.6.11. GAMING
4.1.6.12. INSURANCE
4.1.6.13. CASE STUDY - FRAUD.NET
4.1.6.13.1. AMAZON ML HELPS REDUCE COMPLEXITY AND MAKES SENSE OF EMERGING FRAUD PATTERNS
4.2. What is Machine Learning?
4.2.1. OVERVIEW
4.2.1.1. AI
4.2.1.1.1. ML
4.2.2. USE CASES
4.2.2.1. FRAUD DETECTION
4.2.3. KEY CONCEPTS
4.2.4. ML AND SMART APPS
4.2.5. AMAZON ML
4.2.6. CASE STUDY