Step into healthcare innovation with this comprehensive Nanodegree. Master AI-driven analysis of electronic health records while navigating privacy, bias, and uncertainty to improve medical outcomes effectively.
This lesson will provide you with an Introduction to the EHR Data course outline, content, as well as introduce you to your instructor.
In this lesson, you will learn about the importance of data security and the different standards that apply to EHR, as well as analyzing EHR data.
In this lesson you will learn how to work with different EHR codes and how to map them properly to records.
In this lesson, you'll gain skills in feature engineering and transformation of EHR.
In this final lesson, you'll be putting all of your skills together to build, evaluate and interpret ML models for Bias and Uncertainty.
In this project students will use what they learn in the classroom to apply AI in healthcare for patient data.
Some points apply to every course of this kind; see how we rank.
Data cleaning. Machine learning frameworks in Python. Basic supervised machine learning. Intermediate Python
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
This is CS50W, CS50's Web Programming with Python and JavaScript. Register for free at https://cs50.edx.org/web. Slides and source code at https://cs50.harvard.edu/web. This course picks up where Harvard University’s CS50 leaves off, diving more deeply into the design and implementation of web apps…
Learning objectives This course picks up where Harvard University's CS50 leaves off, diving more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Flask, Django, and Bootstrap. Topics include database design, scalability, security, and user…
Harvard University · edX
Learn the concepts and techniques that make up the foundation of data science and machine learning.
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization m…
