Course Outline

AI for Predictive Modeling in Healthcare

AI-Powered Healthcare Case Studies

Advanced AI Techniques in Healthcare

Data Preprocessing and Feature Engineering

Ethical Considerations in AI for Healthcare

Introduction to AI in Healthcare

Medical Image Analysis with AI

Summary and Next Steps

  • Cleaning and preparing healthcare data
  • Feature engineering techniques for healthcare datasets
  • Dealing with missing and unstructured data
  • Exploring healthcare predictive models
  • Building predictive models using machine learning
  • Evaluating healthcare data models
  • Implementing advanced AI models
  • Exploring natural language processing in healthcare
  • AI-driven decision support systems in healthcare
  • Introduction to AI for medical imaging
  • Implementing deep learning models for image analysis
  • Using AI to detect patterns in medical images
  • Overview of AI applications in healthcare
  • Setting up Google Colab for healthcare AI projects
  • Understanding key healthcare datasets
  • Real-world AI applications in healthcare
  • Case studies on AI-driven predictive analytics
  • Medical image analysis with AI in clinical settings
  • Understanding the ethical impact of AI in healthcare
  • Ensuring privacy and data protection
  • Fairness and transparency in AI models

Requirements

Audience

  • Basic knowledge of AI and machine learning concepts
  • Familiarity with Python programming
  • Understanding of healthcare industry fundamentals
  • Data scientists working in healthcare
  • Healthcare professionals interested in AI
  • Researchers exploring AI-driven healthcare solutions
 14 Hours

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