Course curriculum

    1. AI Usage in Healthcare

    2. Introduction

    3. Overview

    4. Examples of AI in Healthcare

    5. Examples of AI in Healthcare Cont.

    1. Introduction

    2. Overview

    3. Machine Learning Review

    4. Identifying a Classification Scenario

    5. Microsoft Azure

    6. Steps for Classification

    7. Classification Real World Examples

    8. Classification Demonstration Introduction

    9. Policies 1

    10. Policies 2

    11. ML Service Creation

    12. Navigate to ML Studio

    13. ML Studio Tour

    14. Computing Instance

    15. Upload Dataset

    16. Create Dataset

    17. Dataset

    18. Select Columns

    19. Normalization

    20. Split Data

    21. Train Model

    22. Submitting Job

    23. Reviewing Performance

    24. Creating Inference Pipeline

    25. Entering Manual Data

    26. Submitting Inference Pipeline

    27. Initiating Real-time Endpoint

    28. Deploying Model

    29. Testing on New Unseen Data

    1. Introduction to NLP in Healthcare

    2. Overview

    3. Language Service / Studio

    4. Extract Healthcare Model

    5. AI in Healthcare Examples

    6. Healthcare Demonstration Introduction

    7. Language Service Creation

    8. Language Studio Navigation

    9. Testing Azure's Pre-trained Model

    1. Types of Computer Vision

    2. Microsoft Azure

    3. Optical Character Recognition

    4. OCR and Text Analysis

    5. OCR and Translation

    6. AI in Healthcare Scenario

    7. Microsoft Azure Resource Creation

    8. OCR in Healthcare

    1. Introduction

    2. Early Detection of Strokes

    3. Radiogenomics

    4. Diabetes Care

    5. Baricitinib

    6. Congratulations on completing this course!

About this course

  • $850.00
  • 5 Modules
  • 15 hours of learning content

Course Highlights

  • Develop AI-powered healthcare solutions using real-world data

  • Gain hands-on experience with tools like Azure AI, NLP, and computer vision

  • Analyze medical data for insights, diagnostics, and predictive modeling

  • Self-paced, online learning with weekly live support sessions

  • Understand ethical responsibilities when working with healthcare data

  • Connect with professionals in healthcare, data science, and AI fields