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学生对 提供的 AI for Medical Diagnosis 的评价和反馈

1,706 个评分
366 条评论


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. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required! 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 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....



Jul 2, 2020

It was a nice course. Though it covers basics. A follow-up advanced specilization can be made. Overall, it's sufficient for beginner for an engineer trying to learn application of AI for medical field


May 26, 2020

Throughout this course, I was able to understand the different medical and deep learning terminology used. Definitely a good course to understand the basic of image classification and segmentation!


101 - AI for Medical Diagnosis 的 125 个评论(共 367 个)

创建者 Moustafa S

Jul 8, 2020

great course, perfectly organized and have a lot of infos about computer vision and how to tackle 3D images and segmentation

创建者 Gudivada R K

May 15, 2020

Quite interesting topics to learn and the exercises are really good. It could have been better with lecture notes/ slides.

创建者 Rakshit S

Sep 21, 2020

Learned a lot about Segmentation and nitty-gritty of how we can can leverage deep learning technique in medical industry

创建者 ayyüce k

May 16, 2020

It was an amazing experience for me, thank you for all the information! I am proud of a part of the team.

创建者 xuantong Y

Apr 1, 2021

This course is fabulous, encouraging with high-quality material. Everyone who has an interest should enroll in this one.

创建者 Stacy A

Jun 10, 2020

Great real life examples. Explained in a very simple to understand way even for someone without any industry background.

创建者 Parin K

Apr 27, 2020

Very good one. However, it required intermediate python programming skill to fully understand and complete this course.

创建者 Mohamed S E

Apr 13, 2021

Very good overview with focus on realistic challenges with clear description of their origins and also their solutions

创建者 Zanyu S

Jan 18, 2021

Good instructor, detailed instruction, and prepared materials, but a huge difficult leap the final assignment project.

创建者 Karun T

Jun 29, 2020

Excellent introduction to AI in Medicine, with lots of good hands on exercises. Looking forward to Courses 2 and 3 now

创建者 Sunil R

May 15, 2020

Good course. Would highly recommend as a starter course for people looking at getting started in AI for Medical field.

创建者 Dawid D

Apr 26, 2020

Amazing course, I wish it was available a few years ago as it would help me a lot so far! Anyway, really worth taking.

创建者 Sanjeevi G

May 22, 2021

Course was very helpful to understand the classification problem and image tumor segmentation in real medical world

创建者 Olivia M R

May 19, 2020

Amazing method to really learn a LOT! Enjoyed the scope and the application of AI. More like this specialization

创建者 Ankur K A

May 6, 2020

Awesome course and i learned a lot from this course related to medical image preprocessing and other techniques.

创建者 Shiva N R

May 10, 2020

Thanks for the great course, it gave me the well needed boost to start learning AI applications for Medicine.

创建者 Livanos G

Apr 22, 2020

Interesting course with substantive descriptions in many aspects of hands on machine learning implementation.

创建者 Mario A C S

Jul 25, 2020

Excellent course, very useful to tackle practical aspects of deep learning application in real world models.

创建者 Dong Z

Nov 3, 2020

very detailed explanation with hands on guided project! Never had one bad class comes from!

创建者 Neelkant N

Aug 4, 2021

I have learnt a lot from this course. This course is both theortical + Practical . Which didn't bored me.

创建者 Satyam S

May 28, 2020

Excellent Course for Medical Image analysis using CNN and U-net with simple formulae evaluation with code

创建者 Sendo T

May 15, 2020

Very good and a step-by-step instruction and exercise is leading to a deeper and practical undersatnding.

创建者 Jaisil R D

Jun 1, 2020

I really loved this course!!! I learnt a lot !!! Surely this course would help me to finish my project!!

创建者 Abhay S

May 21, 2020

It was an amazing course and taught me how to implement deep learning concepts in the field of medicine.

创建者 Léo M

May 29, 2020

Awesome course! It is essential for those who want to learn about AI and it's applications in medicine.