AI Workloads and Considerations:

AI Workloads and Considerations:

• Understanding the key AI concepts such as machine learning, deep learning, and reinforcement learning.
• Identifying the various workloads that can benefit from AI, such as natural language processing, computer vision, and predictive analytics.
• Exploring considerations for responsible AI, including fairness, transparency, privacy, and security.

Fundamentals of Machine Learning on Azure:

• Understanding the basic principles of machine learning, including supervised, unsupervised, and reinforcement learning.
• Exploring the machine learning process, including data preparation, model training, and model evaluation.
• Introduction to Azure Machine Learning, including creating and deploying models on Azure.

Features of Computer Vision in Azure:

• Exploring Azure's computer vision capabilities, such as image classification, object detection, and facial recognition.
• Understanding how to use Azure Cognitive Services to analyze and interpret images and videos.
• Implementing optical character recognition (OCR) using Azure's computer vision services.

Natural Language Processing (NLP) on Azure:

• Understanding the basics of natural language processing and its applications.
• Using Azure Cognitive Services for NLP tasks like sentiment analysis, language detection, and key phrase extraction.
• Exploring Azure's language understanding service (LUIS) for building conversational AI applications.

Conversational AI on Azure:

• Introduction to Azure Bot Services and how to build and deploy AI-powered chatbots.
• Understanding how to integrate bots with various communication channels like Microsoft Teams, Skype, and websites.
• Exploring the use of the QnA Maker service to build question-and-answer bots.

AI in Knowledge Mining:

• Exploring Azure's tools for knowledge mining, including Azure Search and Cognitive Search.
• Understanding how to enrich and explore data using AI models integrated into search services.
• Implementing AI-driven search solutions for complex data sets.