Online Course – Certified Professional Internship in AI for Medicine by Google, DeepLearning.AI

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Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Practical experience in applying machine learning to concrete problems in medicine
  • In-depth understanding of the application of artificial intelligence in medical cases
  • Working with 2D and 3D medical imaging data
  • Applying tree-based models to improve patient survival estimates
  • Using data from randomized trials to recommend personalized treatments
  • Natural language extraction for importing labels on medical datasets

What you will learn in the course

Courses for which the course is suitable

  • Medical Data Analyst
  • Healthcare software developer
  • Medical Data Scientist
  • Artificial Intelligence Expert in Medicine
  • A doctor with a specialization in medical technologies
  • Health Systems Analyst
  • Medical Algorithm Developer
  • Researcher in the field of artificial intelligence in medicine
  • Healthcare Technology Consultant
  • Healthcare and Technology Project Manager

Internship – Series of 3 courses

Artificial intelligence is transforming the field of medicine. It helps doctors diagnose patients more accurately, predict their future health, and recommend better treatments. This internship series will give you hands-on experience applying machine learning to concrete medical problems.

These courses go beyond the basics of deep learning to teach you the ins and outs of applying AI to medical cases. If you’re new to deep learning or want to build your knowledge of how neural networks work, we recommend enrolling in a deep learning specialization.

Applied Learning Project

Medicine is one of the fastest growing and most important fields, with unique challenges such as dealing with missing data. You will start by learning the intricacies of working with 2D and 3D medical imaging data. You will then apply tree-based models to improve patient survival estimates. You will also use data from randomized trials to recommend treatments that are more appropriate for each individual patient. Finally, you will explore how natural language extraction can more effectively import labels into medical datasets.

Details of the courses that make up the specialization

Artificial intelligence for medical diagnosis

Course 1

Duration: 20 hours
Rating: 4.7 (1,949 ratings)

What will you learn?

Artificial intelligence is transforming the medical field, helping doctors diagnose patients more accurately and recommend better treatments. The course is designed for those familiar with the methodologies and code behind artificial intelligence algorithms.

Curriculum

  • Course 1: Convolutional Neural Network (CNN) models for diagnosing lung and brain problems.
  • Course 2: Risk assessment models and survival estimates for heart disease.
  • Course 3: Predicting the impact of treatment and applying natural language processing.

Skills you will acquire

  • Multi-class classification
  • Image segmentation
  • Machine learning
  • Deep learning
  • Model evaluation

Artificial Intelligence for Medical Prediction

Course 2

Duration: 29 hours
Rating: 4.7 (767 ratings)

What will you learn?

  • Applying tree-based models to estimate patient survival rates.
  • Solving practical challenges in medicine such as missing data.

Skills you will acquire

  • Random forest
  • Machine learning
  • Deep learning
  • Models by time
  • Model tuning

Artificial intelligence for medical care

Course 3

Duration: 22 hours
Rating: 4.7 (515 ratings)

What will you learn?

  • Estimating treatment effects using data from randomized controlled trials.
  • Research into methods for interpreting diagnostic and predictive models.
  • Applying natural language processing to extract information from unstructured medical data.

Skills you will acquire

  • Random forest
  • Exploring the essence of natural language
  • Estimating the effect of treatment
  • The meaning of machine learning
  • Questions and Answers