exams:ai-900 [TEC-ism

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====== AI-900 Azure AI Fundamentals – Study Notes ======

Contributors include Satya for his inital notes.

===== For Study =====
  * [[https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/?practice-assessment-type=certification|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.
  * [[https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-azure-ai-foundry|What is Azure AI Foundry?]]
  * [[https://learn.microsoft.com/en-us/shows/ai-show/azure-machine-learning-studio|Azure Machine Learning Studio – AI Show episodes]]
  * General overview of how **ChatGPT**, **DALL·E**, and **OpenAI** work is helpful.

===== Udemy Resources – Practice Exams =====
  * [[https://ipmc.udemy.com/course/ai-900-azure-ai-fundamentals-practice-tests-course/learn/quiz/6383272#overview|Udemy AI-900 Azure AI Fundamentals – Practice Tests (Course 1)]]
  * [[https://ipmc.udemy.com/course/ai900-azure-ai-fundamentals-test/learn/quiz/5855848/results/?expanded=1656459723|Udemy AI-900 Azure AI Fundamentals – Practice Tests (Course 2)]]

===== 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.