Advance tools and application of AI in Microsoft Azure 254009

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This 5-day intensive course dives deep into the advanced tools and applications of Artificial Intelligence (AI) within the Microsoft Azure cloud platform. Participants will gain hands-on experience with cutting-edge AI services, learn to develop and deploy AI-powered solutions, and explore best practices for building and managing AI initiatives on the Azure platform.

Upon successful completion of this course, participants will be able to:
• Understand the core principles of AI and machine learning.
• Leverage advanced AI services within the Azure ecosystem, such as Azure Machine Learning, Azure Cognitive Services, and Azure OpenAI Service.
• Develop, train, and deploy machine learning models using Azure Machine Learning.
• Integrate AI capabilities into existing applications and workflows.
• Utilize AI for various business applications, including predictive analytics, computer vision, natural language processing, and more.
• Understand and address the ethical and security considerations of AI in the cloud.
• Design and implement AI solutions that are scalable, reliable, and cost-effective.
• Gain practical experience with AI development tools and best practices within the Azure environment.

This course is designed for a diverse audience, including:
• Data Scientists
• Machine Learning Engineers
• Data Engineers
• Software Developers
• IT Professionals
• Solution Architects
• Anyone interested in developing and deploying AI solutions on the Azure platform

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

• Introduction to Artificial Intelligence: Key Concepts, Algorithms, and Applications
• Overview of the Microsoft Azure Platform
• Core AI Services in Azure: Azure Machine Learning, Azure Cognitive Services, Azure OpenAI Service
• Introduction to Cloud Computing and its role in AI

• Data Preparation and Feature Engineering
• Model Training and Evaluation
• Model Deployment and Monitoring
• Hyperparameter Tuning and Model Optimization
• Hands-on Lab: Building and Deploying a Machine Learning Model using Azure Machine Learning

• Computer Vision: Image recognition, object detection, facial recognition
• Natural Language Processing (NLP): Text analysis, sentiment analysis, translation
• Speech Services: Speech-to-text, text-to-speech, speech translation
• Hands-on Lab: Building AI-powered applications using Azure Cognitive Services APIs

• Deep Learning with Azure: Neural networks, deep learning models, and their applications
• AI for Business: Case studies and real-world applications of AI in various industries
• Ethical Considerations in AI: Bias, fairness, and responsible AI development
• Security and Privacy in AI: Protecting data and ensuring the security of AI models

• Emerging Trends in AI: Generative AI, AI for IoT, Edge AI
• The Future of Azure and its role in the AI landscape
• Best Practices for AI Development and Deployment in Azure
• Hands-on Project: Developing a simple AI application using Azure services

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