Instructors: Amirhossein Kiani +3 more

What you'll learn

  •   Diagnose diseases from x-rays and 3D MRI brain images
  •   Predict patient survival rates more accurately using tree-based models
  •   Estimate treatment effects on patients using data from randomized trials
  •   Automate the task of labeling medical datasets using natural language processing

    • Skills you'll gain

  •   Image Analysis
  •   Random Forest Algorithm
  •   Clinical Trials
  •   Treatment Planning
  •   Data Processing
  •   Applied Machine Learning
  •   Data Analysis
  •   Machine Learning
  •   Machine Learning Methods
  •   Deep Learning
  •   Precision Medicine
  •   Risk Modeling

    • Specialization - 3 course series

    Medicine is one of the fastest-growing and important application areas, with unique challenges like handling missing data. You’ll start by learning the nuances of working with 2D and 3D medical image data. You’ll then apply tree-based models to improve patient survival estimates. You’ll also use data from randomized trials to recommend treatments more suited to individual patients. Finally, you’ll explore how natural language extraction can more efficiently label medical datasets.

    This program will give you practical experience in applying cutting-edge machine learning techniques to concrete problems in modern medicine: - In Course 1, you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders. - In Course 2, you will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis. - In Course 3, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports. These courses go beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. As a learner, you will be set up for success in this program if you are already comfortable with some of the math and coding behind AI algorithms. You don't need to be an AI expert, but a working knowledge of deep neural networks, particularly convolutional networks, and proficiency in Python programming at an intermediate level will be essential. If you are relatively new to machine learning or neural networks, we recommend that you first take the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng. The demand for AI practitioners with the skills and knowledge to tackle the biggest issues in modern medicine is growing exponentially. Join us in this specialization and begin your journey toward building the future of healthcare.

    AI for Medical Prognosis

    AI For Medical Treatment

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