Month 13: Cloud Machine Learning and AI

Week 1: Introduction to Machine Learning and AI

Day 1: Understanding Machine Learning (ML) and Artificial Intelligence (AI)

Day 2: ML vs. AI: similarities and differences

Day 3: Types of ML: supervised, unsupervised, and reinforcement learning

Day 4: AI applications and use cases in the cloud

Day 5: Hands-on activity: Exploring popular ML and AI tools and libraries

Week 2: AWS ML and AI Services

Day 1: Introduction to AWS SageMaker

Day 2: Hands-on activity: Building, training, and deploying a model with SageMaker

Day 3: Introduction to AWS Rekognition

Day 4: Hands-on activity: Image and video analysis with Rekognition

Day 5: Introduction to AWS Lex

Day 6: Hands-on activity: Building a chatbot with Lex

Week 3: Azure ML and AI Services

Day 1: Introduction to Azure Machine Learning

Day 2: Hands-on activity: Building, training, and deploying a model with Azure Machine Learning

Day 3: Introduction to Azure Cognitive Services

Day 4: Hands-on activity: Using Azure Cognitive Services for AI-powered applications

Day 5: Recap and Q&A session

Week 4: GCP ML and AI Services

Day 1: Introduction to GCP AI Platform

Day 2: Hands-on activity: Building, training, and deploying a model with AI Platform

Day 3: Introduction to GCP AutoML

Day 4: Hands-on activity: Using AutoML for custom ML models

Day 5: Introduction to GCP Dialogflow

Day 6: Hands-on activity: Building a chatbot with Dialogflow

Week 5: ML Model Training, Deployment, AI Ethics, and Bias Considerations

Day 1: Best practices for ML model training and deployment in the cloud

Day 2: Hands-on activity: Deploying a trained ML model to production

Day 3: Introduction to AI ethics and bias considerations

Day 4: Strategies for addressing and mitigating bias in AI systems