AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
Este curso forma parte de Programa especializado: IA para medicina
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ofrecido por
Programa especializado: IA para medicinadeeplearning.aiAcerca de este Curso
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Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 3 en
Nivel intermedio
We recommend first completing Course 1 and 2 of the AI For Medicine Specialization.
Aprox. 23 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
Qué aprenderás
Estimate treatment effects using data from randomized control trials
Explore methods to interpret diagnostic and prognostic models
Apply natural language processing to extract information from unstructured medical data
Habilidades que obtendrás
- treatment effect estimation
- machine learning interpretation
- Random Forest
- natural language entity extraction
- question-answering
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 3 de 3 en
Nivel intermedio
We recommend first completing Course 1 and 2 of the AI For Medicine Specialization.
Aprox. 23 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programa - Qué aprenderás en este curso
9 horas para completar
Treatment Effect Estimation
In this week, you will learn: How to analyze data from a randomized control trial, interpreting multivariate models, evaluating treatment effect models, and interpreting ML models for treatment effect estimation.
9 horas para completar
12 videos (Total 43 minutos), 3 lecturas, 3 cuestionarios
7 horas para completar
Medical Question Answering
In this week, you will learn how to extract disease labels from clinical reports, and also question answering with BERT.
7 horas para completar
10 videos (Total 32 minutos)
7 horas para completar
ML Interpretation
In this week, you will learn how to interpret deep learning models, and also feature importance in machine learning.
7 horas para completar
9 videos (Total 29 minutos), 3 lecturas, 2 cuestionarios
Reseñas
Principales reseñas sobre AI FOR MEDICAL TREATMENT
por KN7 de dic. de 2020
The assignment for the first week was out of scope for the course in my opinion. It was too much focused on a good handling of pandas which is rather difficult for people who are not experts in pandas
por AA23 de jun. de 2020
A bit tough, but well laid and well explained.Overall the entire specialization was very good. However it misses in depth theory . But overall a very good course with practical applications
por RB12 de jun. de 2020
A very nice course and specialization as well. Offers so much to learn even for those who are pure machine learners.Instructors were fantastic.Assignments were challenging but excellent.
por AS7 de jun. de 2020
Fantastic coursework teaching fundamentals required for analysis of medical domain data. Quality content with great assignments. Level of difficulty is intermediate for the assignments.
Acerca de Programa especializado: IA para medicina
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
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