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AI-900 Azure AI Fundamentals – Study Notes

For Study

Udemy Resources – Practice Exams

Core Concepts & Notes

Machine Learning Basics

  • 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.

Datasets, Features & Labels

  • 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).

Machine Learning Types (Summary)

  • 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

  • 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.

Natural Language Processing (NLP)

  • 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.

Azure OpenAI & Generative AI Services

  • 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 Services

  • 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.

Principles of Responsible AI

  • 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.

Azure Machine Learning Designer

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

Additional Key Points

  • 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.