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Comentários e feedback de alunos de Fundamentals of Machine Learning for Healthcare da instituição Universidade de Stanford

234 classificações

Sobre o curso

Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles. This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare. The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies. Co-author: Geoffrey Angus Contributing Editors: Mars Huang Jin Long Shannon Crawford Oge Marques The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content....

Melhores avaliações


8 de set de 2020

Amazing course teaching the innumerous opportunities in the healthcare sector and the application of AI in the same. Beautifully drafted course with intriguing tutorials and exercises.


1 de abr de 2021

This was a great course, the presenters really gave a clear view about the differences which could happen when working with health related data set. Very well done,

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26 — 50 de 68 Avaliações para o Fundamentals of Machine Learning for Healthcare

por Raimundo N

28 de mar de 2022

So grateful for this learning journey with the prestigious Stanford!

por hani j

5 de out de 2021

it is a really good course for learning ML but some of the videos are a bit hard to fully understand

por Marcelo M C

15 de jul de 2022

The course is very good. Great content, very well structured and with key insights. The instructors are great, very clear explanations. It covers both fundamentals of ML as well as principles of its application to healthcare. I have background already in ML and it helped me reinforce concepts as well as learn an overall point of view of health related benefits, opportunities, use cases, challenges and risks. Very valuable information for an introductory course.

However, for people completely new to ML, I think it could be challenging to follow all the concepts that are presented. Coding is not required nor deep math; in fact, instructors do a great work trying to abstract the details in order to explain the concepts. But in ML, some of the topics require more depth and background in order to be able to fully grasp the idea. So if this is your case, it could be good to get some extra content from elsewhere on the basics before or during the course.

por Jau-Jie Y

12 de jul de 2021

I would like to thanks to both instructor, Professor Matthew Lungren and Professor Serena Yeung. They explain fairly clear of some concept, and it help me much. I mistake some ideal of cross entropy, loss function, etc.

And how to solve the underfitting/overfitting section is very useful.

Special thanks to both teachers.

por Jonathan W

2 de fev de 2022

Through out the course real world examples are shared to provide context. The recommended reading provides both broader and deeper insight. As a non physician I found the ethics papers really interesting read and helped provide me with greater perspective of some of the challenges in healthcare.

por Gonzalo R

19 de jan de 2022

Very interesting introductory course about ML in Healthcare, with a good introduction in the statistical key concepts to understand the way hoy ML works and things to care about to reduce errors and biases.

por Sandro M

20 de ago de 2021

Conteúdo ótimo! Traz uma boa base de conceitos e aplicações para qualquer profissional que queira entender as aplicações de machine learning na área de saúde.

por María F R E

16 de jan de 2022

A​lthough it is said is just basic stuff, it changed my way of analyzing the papers of AI in medicine

por Chetan D

4 de mar de 2021

Excellent introductory course to understand Machine Learning in the context of Healthcare delivery

por Mike W

4 de dez de 2020

great overview to explain ML to all members of a team developing healthcare applications of AI

por Kushal A S

17 de out de 2020

Nicely Framed and Executed in a simple language so anyone can catch up earliest.

por Sabine F

1 de ago de 2022

Great course, really pinned down the essentials needed to continue my ML path.

por Kent H

12 de jan de 2021

Great course. Thank you so much for the time and effort putting it together.

por Kedir O

30 de jul de 2022

The most precise and concise lecture ever in regading machine learning!

por NADY E B

6 de dez de 2020

A bit too technical yet very interesting. Excellent course. Thanks!

por BALU P

19 de jul de 2021

great instructors and all concepts explained in very easy terms

por blue a

20 de dez de 2020

Tremendous learning and outstanding presentation of concepts.

por Andrew M

16 de jul de 2022

E​xcellent course; the instructors are particularly good.

por Ann V G

3 de out de 2020

An excellent introduction. Concise. Helpful citations.

por Vera S

20 de out de 2021

The instructors are both so knowledgeable and adorable!

por Huma P

7 de jun de 2022

Great explanation of Machine Learning in Healthcare.

por Anton L

21 de out de 2020

Outstanding team performance by the two lecturers

por Lori S

14 de mar de 2021

"a labor of love' indeed; wonderful ! thank you!

por Vincent C

10 de nov de 2021

Amazing Good instructors, i really enjoyed them

por Kabakov B

6 de out de 2020

101 to ML. Like Ng's book ML Yearning.