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Azure Artificial Intelligence
Artificial-intelligence Overview
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    • Understand machine learning  
    • Understand computer vision  
    • Understand natural language processing  
    • Understand document intelligence and knowledge mining  
    • Understand generative AI  
    • Challenges and risks with AI  
    • Understand Responsible AI  
    • Introduction to Cognitive Services  
    • Ethical Considerations in AI  
    • Introduction to Azure Notebooks and Jupyter Notebooks in Azure  
Cognitive Services in Azure
    • Vision APIs (Computer Vision, Face API, Custom Vision)  
    • Speech APIs (Speech to Text, Text to Speech, Speech Translation)  
    • Language APIs (Text Analytics, Translator, QnA Maker)  
    • Decision APIs (Personalizer, Anomaly Detector)  
    • Integration of Cognitive Services in Applications  
Azure Machine Learning Fundamentals
    • Azure Machine Learning Workspace  
    • Feature Engineering in Azure ML  
    • Model Training and Hyperparameter Tuning  
    • Model Evaluation and Deployment  
Deep Learning with Azure
    • Introduction to Deep Learning  
    • Using Azure Machine Learning for Deep Learning  
    • Neural Networks and TensorFlow in Azure  
    • Distributed Training with Azure ML  
    • Model Deployment for Deep Learning Models  
Natural Language Processing (NLP) in Azure
    • Text Analytics and Sentiment Analysis  
    • Named Entity Recognition (NER)  
    • Language Understanding (LUIS)  
    • Building a Chatbot with Azure Bot Service  
    • Language Translation with Azure Translator  
Azure AI Model Interpretability and Explainability
    • Interpreting Machine Learning Models  
    • Azure Machine Learning Interpretability SDK  
    • Model Explainability Techniques  
    • Addressing Bias and Fairness in AI  
    • Responsible AI Practices  
Reinforcement Learning with Azure
    • Introduction to Reinforcement Learning  
    • Azure Machine Learning for Reinforcement Learning  
    • Building and Training Reinforcement Learning Models  
    • Simulation Environments in Azure  
    • Real-world Applications of Reinforcement Learning  
Azure AI Services Integration
    • Integrating Cognitive Services with Azure ML  
    • Building End-to-End AI Pipelines  
    • Orchestrating Workflows with Azure Logic Apps  
    • Azure Functions and AI Integration  
    • Monitoring and Logging AI Services in Azure  
AI Model Deployment and Management
    • Deployment Options for AI Models  
    • Azure Kubernetes Service (AKS) for AI Workloads  
    • Model Versioning and A/B Testing  
    • Continuous Integration and Deployment for AI  
    • Monitoring and Scaling AI Deployments  
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Sam

Bachelor of Technology

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