Self- [AI-900] Microsoft Azure AI Fundamentals

What Is AI/ML?

In computer science, artificial intelligence (AI), is intelligence demonstrated by machines. AI techniques enable computers to mimic human intelligence, including the use of logic & decision making. Machine learning algorithms are applied upon data both structured and unstructured to produce these artificially intelligent (AI) systems.

  • Machine learning is a subset of artificial intelligence where statistical methods are used to help a computer improve at a task with training and experience. Machine learning algorithms are used in a wide variety of applications, such as email filtering, computer vision, natural language processing, and chat-bot creation.
  • Deep learning is a subset of machine learning including multi-layer neural networks to train models.

Why AI on Azure?

  • Gives a cloud platform for implementing AI Solutions
  • Provide no-code ML models for processing data.
  • Implement and monitor AI solutions
  • We can design AI as cost-effective Intelligent Edge solutions.
  • We can design and identify data governance, and requirements.
  • Allow integration of ML models with other azure services.

Introducing: [AI-900] Microsoft Azure AI Fundamentals Training

Become an expert in [AI-900] Microsoft Azure AI Fundamentals by mastering these 12 critical core skills…


img

Common AI Workloads
img

Guiding Principles For AI
img

Machine Learning Types
img

Core Machine Learning Concepts
img

Core Tasks In Creating a Machine Learning
img

Capabilities of No-code ML
img

Computer Vision Solution Types
img

Azure Tools & Services
img

Common NLP Workload Scenarios
img

Azure Tools & Services for NLP Workloads
img

Use Cases for Conversational AI
img

Azure Services for Conversational AI

COURSE BREAKDOWN


  1. IDENTIFY FEATURES OF COMMON AI WORKLOADS
    • 1.1 IDENTIFY PREDICTION/FORECASTING WORKLOADS
    • 1.2 IDENTIFY FEATURES OF ANOMALY DETECTION WORKLOADS
    • 1.3 IDENTIFY COMPUTER VISION WORKLOADS
    • 1.4 IDENTIFY NATURAL LANGUAGE PROCESSING OR KNOWLEDGE MINING WORKLOADS
    • 1.5 IDENTIFY CONVERSATIONAL AI WORKLOADS
  2. IDENTIFY GUIDING PRINCIPLES FOR RESPONSIBLE AI
    • 2.1 DESCRIBE CONSIDERATIONS FOR FAIRNESS IN AN AI SOLUTION
    • 2.2 DESCRIBE CONSIDERATIONS FOR RELIABILITY AND SAFETY IN AN AI SOLUTION
    • 2.3 DESCRIBE CONSIDERATIONS FOR PRIVACY AND SECURITY IN AN AI SOLUTION
    • 2.4 DESCRIBE CONSIDERATIONS FOR INCLUSIVENESS IN AN AI SOLUTION
    • 2.5 DESCRIBE CONSIDERATIONS FOR TRANSPARENCY IN AN AI SOLUTION
    • 2.6 DESCRIBE CONSIDERATIONS FOR ACCOUNTABILITY IN AN AI SOLUTION

  1. IDENTIFY COMMON MACHINE LEARNING TYPES
    • 1.1 IDENTIFY REGRESSION MACHINE LEARNING SCENARIOS
    • 1.2 IDENTIFY CLASSIFICATION MACHINE LEARNING SCENARIOS
    • 1.3 IDENTIFY CLUSTERING MACHINE LEARNING SCENARIOS
  2. DESCRIBE CORE MACHINE LEARNING CONCEPTS
    • 2.1 IDENTIFY FEATURES AND LABELS IN A DATASET FOR MACHINE LEARNING
    •   2.2 DESCRIBE HOW TRAINING AND VALIDATION DATASETS ARE USED IN MACHINE LEARNING
    • 2.3 DESCRIBE HOW MACHINE LEARNING ALGORITHMS ARE USED FOR MODEL TRAINING
    • 2.4 SELECT AND INTERPRET MODEL EVALUATION METRICS FOR CLASSIFICATION AND REGRESSION
  3. IDENTIFY CORE TASKS IN CREATING A MACHINE LEARNING SOLUTION
    • 3.1 DESCRIBE COMMON FEATURES OF DATA INGESTION AND PREPARATION
    • 3.2 DESCRIBE COMMON FEATURES OF FEATURE SELECTION AND ENGINEERING
    • 3.3 DESCRIBE COMMON FEATURES OF MODEL TRAINING AND EVALUATION
    • 3.4 DESCRIBE COMMON FEATURES OF MODEL DEPLOYMENT AND MANAGEMENT
  4. DESCRIBE CAPABILITIES OF NO-CODE MACHINE LEARNING WITH AZURE
    • 4.1 AUTOMATED MACHINE LEARNING TOOL
    • 4.2 AZURE MACHINE LEARNING DESIGNER
  5. ACTIVITY GUIDE: CREATE AZURE ML WORKSPACE
  6. ACTIVITY GUIDE: CREATE CLASSIFICATION MODEL USING AZURE ML DESIGNER

  1. IDENTIFY COMMON TYPES OF COMPUTER VISION SOLUTION
    • 1.1 IDENTIFY FEATURES OF IMAGE CLASSIFICATION SOLUTIONS
    • 1.2 IDENTIFY FEATURES OF OBJECT DETECTION SOLUTIONS
    • 1.3 IDENTIFY FEATURES OF SEMANTIC SEGMENTATION SOLUTIONS
    • 1.4 IDENTIFY FEATURES OF OPTICAL CHARACTER RECOGNITION SOLUTIONS
    • 1.5 IDENTIFY FEATURES OF FACIAL DETECTION, RECOGNITION, AND ANALYSIS SOLUTIONS
  2. IDENTIFY AZURE TOOLS AND SERVICES FOR COMPUTER VISION TASKS
    • 2.1 IDENTIFY CAPABILITIES OF THE COMPUTER VISION SERVICE
    • 2.2 IDENTIFY CAPABILITIES OF THE CUSTOM VISION SERVICE
    • 2.3 IDENTIFY CAPABILITIES OF THE FACE SERVICE
    • 2.4 IDENTIFY CAPABILITIES OF THE FORM RECOGNIZER SERVICE
  3. ACTIVITY GUIDE: IMAGE CLASSIFICATION WITH CUSTOM VISION SERVICE

  1. IDENTIFY FEATURES OF COMMON NLP WORKLOAD SCENARIOS
    • 1.1 IDENTIFY FEATURES AND USES FOR KEY PHRASE EXTRACTION
    • 1.2 IDENTIFY FEATURES AND USES FOR ENTITY RECOGNITION
    • 1.3 IDENTIFY FEATURES AND USES FOR SENTIMENT ANALYSIS
    • 1.4 IDENTIFY FEATURES AND USES FOR LANGUAGE MODELING
    • 1.5 IDENTIFY FEATURES AND USES FOR SPEECH RECOGNITION AND SYNTHESIS
    • 1.6 IDENTIFY FEATURES AND USES FOR TRANSLATION
  2. IDENTIFY AZURE TOOLS AND SERVICES FOR NLP WORKLOADS
    • 2.1 IDENTIFY CAPABILITIES OF THE TEXT ANALYTICS SERVICE
    • 2.2 IDENTIFY CAPABILITIES OF THE LANGUAGE UNDERSTANDING INTELLIGENCE SERVICE (LUIS)
    • 2.3 IDENTIFY CAPABILITIES OF THE SPEECH SERVICE
    • 2.4 IDENTIFY CAPABILITIES OF THE TEXT TRANSLATOR SERVICE
  3. ACTIVITY GUIDE: SENTIMENT ANALYSIS

  1. IDENTIFY COMMON USE CASESFOR CONVERSATIONAL AI
    • 1.1 IDENTIFY FEATURES AND USES FOR WEBCHAT BOTS
    • 1.2 IDENTIFY FEATURES AND USES FOR TELEPHONE VOICE MENUS
    • 1.3 IDENTIFY FEATURES AND USES FOR PERSONAL DIGITAL ASSISTANTS
  2. IDENTIFY AZURE SERVICES FOR CONVERSATIONAL AI
    • 2.1 IDENTIFY CAPABILITIES OF THE QNA MAKER SERVICE
    • 2.2 IDENTIFY CAPABILITIES OF THE BOT FRAMEWORK
  3. ACTIVITY GUIDE: CREATE, TRAIN AND PUBLISH QNA MAKER KNOWLEDGE BASE