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Artificial Intelligence for Healthcare Specialization

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Artificial Intelligence for Healthcare Specialization

Build the Future of Healthcare with AI. Learn to design, implement, and evaluate responsible AI solutions that improve patient outcomes

SkillUp
Ramesh Sannareddy

Instructors: SkillUp

Included with Coursera Plus

Get in-depth knowledge of a subject
Intermediate level

Recommended experience

12 weeks to complete
at 3 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

12 weeks to complete
at 3 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Describe how to apply machine learning techniques to analyze healthcare data and solve clinical problems

  • Evaluate the effectiveness of AI models in healthcare contexts, including performance metrics, clinical validation, and ethical considerations

  • Develop predictive models for patient outcomes using supervised and unsupervised learning techniques

  • Design and implement neural networks for medical image analysis and clinical decision support systems

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Taught in English
Recently updated!

November 2025

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Specialization - 3 course series

What you'll learn

  • Describe how AI and machine learning are transforming healthcare delivery, clinical workflows, and patient outcomes.

  • Explain ethical frameworks, regulations, and governance standards relevant to AI in healthcare.

  • Summarize common challenges and solutions related to bias, privacy, and integration in AI healthcare implementation.

  • Design and implement machine learning workflows tailored to healthcare datasets and requirements.

Skills you'll gain

Category: Healthcare Industry Knowledge
Category: Clinical Research
Category: Clinical Informatics
Category: Artificial Intelligence
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Health Informatics
Category: Data Analysis

What you'll learn

  • Define supervised and unsupervised machine learning techniques used for healthcare datasets.

  • Apply preprocessing, feature engineering, and class-imbalance management techniques to real healthcare datasets.

  • Design and implement supervised learning models for disease prediction and clinical decision support tasks.

  • Evaluate model performance using precision-recall metrics, calibration, and external validation.

Skills you'll gain

Category: Clinical Informatics
Category: Electronic Medical Record System
Category: Machine Learning
Category: Statistical Machine Learning
Category: Health Informatics
Category: Predictive Modeling
Category: Healthcare Ethics
Category: Data Analysis
Category: Machine Learning Algorithms
Category: Machine Learning Methods
Category: Data Processing
Category: Data Mining
Category: Deep Learning

What you'll learn

  • Identify key AI technologies in healthcare, including NLP, generative AI, and computer vision.

  • Explain how advanced AI models process clinical text, patient data, and medical images.

  • Analyze healthcare datasets to design and evaluate AI models using guided Jupyter labs.

  • Create an integrated AI solution combining NLP, generative AI, and computer vision tools.

Skills you'll gain

Category: Jupyter
Category: Decision Support Systems
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Health Systems
Category: Health Information Management
Category: Data Analysis
Category: Multimodal Prompts

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Instructors

SkillUp
SkillUp
113 Courses357,677 learners

Offered by

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