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Batch
Date: Oct
16th @8:00AM
Faculty: Mr. Sekhar Reddy (15+ Yrs of Exp,.. & Real Time Expert)
Duration: 35 Days
Venue
:
DURGA SOFTWARE SOLUTIONS,
Flat No : 202,
2nd Floor,
HUDA Maitrivanam,
Ameerpet, Hyderabad - 500038
Ph.No: +91- 9246212143, 80 96 96 96 96
Syllabus:
Azure AI Fundamentals certification (Exam AI-900)
The Azure AI Fundamentals certification (Exam AI-900) covers a range of topics to help you understand and demonstrate fundamental AI concepts related to Microsoft Azure
To get started with Azure AI, you can explore the Microsoft Azure AI Fundamentals training path. This training provides a comprehensive introduction to AI concepts and how they are implemented using Azure services. Here’s an overview of what you can expect:
1. Artificial Intelligence Workloads and Considerations
Identify features of common AI workloads:
- Machine Learning:
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Reinforcement learning
- Anomaly Detection:
- Outlier detection
- Fraud detection
- Computer Vision:
- Image classification
- Object detection
- Semantic segmentation
- Natural Language Processing (NLP):
- Text classification
- Named entity recognition
- Sentiment analysis
- Conversational AI:
- Chatbots
- Virtual assistants
Identify guiding principles for responsible AI:
- Fairness:
- Bias detection and mitigation
- Reliability and Safety:
- Robustness
- Error handling
- Privacy and Security:
- Data protection
- Secure data handling
- Inclusiveness:
- Accessibility
- Diverse datasets
- Transparency:
- Explainability
- Interpretability
- Accountability:
- Governance
- Ethical considerations
2. Fundamental Principles of Machine Learning on Azure
Describe core machine learning concepts:
- Regression:
- Linear regression
- Logistic regression
- Classification:
- Decision trees
- Support vector machines
- Clustering:
- K-means clustering
- Hierarchical clustering
- Reinforcement Learning:
- Markov decision processes
- Q-learning
Identify Azure tools and services for machine learning:
- Azure Machine Learning:
- Designer
- Automated ML
- Notebooks
- Azure Databricks:
- Collaborative notebooks
- Spark-based analytics
- Azure Synapse Analytics:
- Integrated analytics
- Data warehousing
3. Features of Computer Vision Workloads on Azure
Identify common types of computer vision solutions:
- Image Classification:
- Pre-trained models
- Custom models
- Object Detection:
- Bounding boxes
- Instance segmentation
- Optical Character Recognition (OCR):
- Text extraction
- Document processing
- Facial Recognition:
- Face detection
- Emotion recognition
Identify Azure tools and services for computer vision tasks:
- Azure Computer Vision:
- Custom Vision:
- Custom image classification
- Object detection
- Face API:
- Face detection
- Face verification
- Form Recognizer:
- Form processing
- Receipt recognition
4. Features of Natural Language Processing (NLP) Workloads on Azure
Describe features of NLP workloads:
- Text Analytics:
- Sentiment analysis
- Key phrase extraction
- Language Understanding (LUIS):
- Intent recognition
- Entity extraction
- Speech Recognition:
- Speech-to-text
- Text-to-speech
- Translation:
- Text translation
- Speech translation
Identify Azure tools and services for NLP:
- Azure Text Analytics:
- Sentiment analysis
- Language detection
- Azure Translator:
- Text translation
- Document translation
- Azure Speech:
- Speech-to-text
- Text-to-speech
- Language Understanding (LUIS):
- Intent recognition
- Entity extraction
5. Features of Generative AI Workloads on Azure
Describe features of generative AI workloads:
- Text Generation:
- Language models
- Text completion
- Image Generation:
- GANs (Generative Adversarial Networks)
- Style transfer
- Code Generation:
- Code completion
- Code synthesis
Identify Azure tools and services for generative AI:
- Azure OpenAI Service:
- Azure Cognitive Services:
- Customizable AI services
- Pre-built AI models
