AI-900 Azure AI Fundamentals – Study Notes

Contributors include Satya for his inital notes.

• Microsoft Learn – Azure AI Fundamentals (AI-900)
  • Take the practice assessment exams until you regularly score 90%+.
• AI-900 full course training videos on Microsoft Learn and YouTube.
• What is Azure AI Foundry?
• Azure Machine Learning Studio – AI Show episodes
• General overview of how ChatGPT, DALL·E, and OpenAI work is helpful.

• Udemy AI-900 Azure AI Fundamentals – Practice Tests (Course 1)
• Udemy AI-900 Azure AI Fundamentals – Practice Tests (Course 2)

• Regression algorithms are used to predict numeric values.
• Classification algorithms are used to predict categories (which class an input belongs to).
• Clustering algorithms group data points that have similar characteristics.
• Supervised learning uses labeled training data (features + labels).
• Unsupervised learning uses unlabeled data and includes clustering, not regression or classification.
• K-Means clustering is an unsupervised algorithm used for training clustering models.

• Features = input variables used by the model.
• Labels = target values the model predicts.
• Training dataset – features and known label values (used to train the model).
• Validation dataset – features and known label values (used to tune and evaluate the model).

• Supervised learning (training data is labeled):
  • Regression – label is numeric.
  • Classification – label is a category or class.
    • Binary classification – two classes (True/False, Yes/No).
    • Multiclass classification – more than two classes.
• Unsupervised learning (training data is unlabeled):
  • Clustering – grouping similar items together.

• Computer Vision is used to extract information from images, but it is not a search and indexing solution by itself.
• Semantic segmentation is used to classify individual pixels in an image.
• OCR (Optical Character Recognition) and Spatial Analysis are part of the Azure AI Vision service.
• Object detection provides the ability to generate bounding coordinates (bounding boxes) as part of its output.

• Stemming or lemmatization normalizes words for counting and analysis.
• Frequency analysis counts how often a word appears in a text.
• N-grams extend frequency analysis to multi-term phrases.
• Vectorization represents words/documents as vectors in N-dimensional space to capture relationships.
• Extracting key phrases from text helps identify the main terms in NLP.
• Data mining workloads focus on searching and indexing large amounts of data.
• Knowledge mining is an AI workload that makes large amounts of data searchable.
• Conversational AI is part of NLP and facilitates the creation of chatbots.
• Language models predict the next word in a sequence of words based on context.

• DALL·E generates images from natural language prompts.
• GPT-3 / GPT-3.5 can understand natural language and code, but do not generate images.
• Embeddings convert text into numeric vector representations, used to:
  • Classify text
  • Search text
  • Compare similarity between texts
• Whisper can transcribe and translate speech.
• GPT models are strong at understanding and creating natural language.
• System messages (in chat-style interactions) define constraints, style, and behavior for Gen AI responses.

• Speech recognition converts spoken language into text.
• Speech recognition can use audio data to identify distinct user voices.
• Speech synthesis (Text-to-Speech, TTS) converts written text into spoken language.
• Conversational AI–enabled devices can:
  • Engage in natural language conversations with users.
  • Understand user queries and provide relevant responses.
  • Make interactions more human-like and intuitive.

• Accountability – systems are designed to meet ethical and legal standards.
• Privacy and Security – protect any personal and/or sensitive data.
• Inclusiveness – empower people in a positive and engaging way.
• Fairness – ensure that all users of the system are treated fairly.

• Create a pipeline before using Machine Learning Designer to train a model.
• Classification, regression, and time-series forecasting are all supervised machine learning models.

• Computer Vision:
  • Used for image understanding, not for full search/indexing solutions on its own.
• Azure AI Vision:
  • Includes OCR and Spatial Analysis capabilities.
• Knowledge Mining & Data Mining:
  • Focus on searching, indexing, and making data searchable across large content stores.