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AI for Medical Diagnosis に戻る

deeplearning.ai による AI for Medical Diagnosis の受講者のレビューおよびフィードバック

4.7
1,166件の評価
262件のレビュー

コースについて

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 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....

人気のレビュー

RK

Jul 03, 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

KH

May 27, 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!

フィルター:

AI for Medical Diagnosis: 26 - 50 / 260 レビュー

by omiya h

Apr 28, 2020

I learned a lot from this course. Each lab, assignments, and weekly quizzes enabled me to take a deeper dive into how these models and image processing work on medical images. It made me wear my thinking cap and think deeply into each parameters and features and what mathematical-statistical models are used for prediction and classification analysis!

by Luka

Jul 06, 2020

It was nice to attend this course, mostly due to clear examples, good visual representation of examples and a lot of practical exercises that served as nice preparation for assignments.

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by Peter S

Jul 07, 2020

This is the best course of the specialization. The instructor created one of the best models for chest X-ray diagnosis that was the first model that beat human radiologists in detecting pneumonia (now with COVID-19 that's more important than ever). The original CheXNet model is flawlessly and simply explained so that anyone could understand it with all details served literally on a plate requiring no additional work. This is my favorite course of all DeepLearning.ai specializations! Thanks Pranav & Andrew!

by Asad K

Jun 19, 2020

Extremely well-written content/code and short but illuminating lectures and discussions. Good terse discussions of common metrics, issues with imbalanced datasets, and interesting ways of tackling those issues, U-Net architecture and loss functions for semantic segmentation, and exploration of medical datasets.

by Jeiran C

May 11, 2020

Thanks for gathering all the useful material for using AI in medical imaging. I come from the medical imaging background, and I can't express how useful and precise were your teaching materials. I also would like to thank the Slack support system for all the useful hints on how to solve the assignments.

by Santiago I C

Apr 22, 2020

Good course!! The clasification part is similar to classification in other courses (such as tensorflow course from DL.ai) but some medical basics. Good introductory and some good tips. The segmentation part is very explainatory as it's really what's needed to begin in real practice. Keep up! Recommended

by Murtala

Apr 23, 2020

It is been my dream to apply AI in healthcare. This incredible course has given me the knowledge that I need to approach medical image data from preprocessing to model development, and prediction. I also learnt some radiological and medical jargons along the way.

Thank you so much Deeplearning.ai.

by Rangel I A W

Apr 17, 2020

The course teach me to consider some flaws that i had made back in the preprocessing step and is a good refresher of the metrics of clasification models. Also, the brain mri image segmentation assignment was very special because it serves as a starting point for input a voxel in a neural net.

by Bharathi k N

Jul 29, 2020

It is really a great course on applying machine learning and deep learning to medical field. The video lectures are short and easy to follow. Assignments are so great. Really looking forward to take the following courses. Thank you deeplearning.ai and coursera for this amazing course.

by Rao F M

Aug 19, 2020

An excellent insight of Medical Image Processing, highly recommended for those who are working on Medical Images. Segmentation and boundary delineation was not covered in details, maybe in the follow up courses this aspect will be covered more deliberately. Thank you coursera .

by Ganapathy S

Apr 22, 2020

Overall courses is very good though the course is short with respect to video lecture and course material the assignments are bit lengthy and tough. It would be good to have assignment code walk through as we have refer multiple outside materials w.r.to python coding.

by Steve S

May 14, 2020

Excellent well metered presentations and quizzes. The final quiz really made you think and understand the entire process. I tried not to use the discussions, however, as the final quiz submission was getting close, reached out to Mubashar. Zeroed in on my issue!

by Sagar K

May 10, 2020

Really good course for learning to train biased and complex image datasets. I was waiting for this course since it was first introduced on Youtube deeplearning.ai. Feeling really satisfied that the course was exactly the same level I was expecting it to be.

by Kian E O

Sep 02, 2020

Excellent course which introduces key concepts of AI in medical diagnosis. Concepts are explained in a clear and effective manner for both videos and labs. Videos are extremely bit-sized and to the point. Good for beginners. One of the best courses around.

by Kanisk U

May 08, 2020

Awesome course. In such a short period of time, I got up and running on the medical image classification. Though, this is just scratching the surface, but hope to use this course base my experiments on. Thanks Andrew Ng & Pranav for starting this course.

by Surya J

Jun 01, 2020

Though I'm not from a medical background, the ideas and concepts explained in this course were very insightful and relate to problems in my domain. Great thanks to the deeplearning ai team and Coursera for putting together a wonderful course yet again.

by Mei-Ling F

Aug 08, 2020

Very detailed yet elementary introduction of AI application in Healthcare. I have taken more advanced courses in the university, and I wish to know this course way before I have taken one. I enjoyed it a lot and it helped me bridge the gap. Thank you!

by Navodini W

May 24, 2020

This course is well organized and have a good flow, that helps to understand all the facts. The assignments are also good and have a lot to learn. Thank you very much for providing a platform for students to learn this area, AI in medical applications.

by Ashish K

May 22, 2020

I came here to know and build model that actually help patients in detecting the diseases and i have got the skills . Coursera is a platform where you not only read, but it also give you the chances to interact with people who are expert in this field.

by Irina G

Jun 22, 2020

This is an excellent course. It teaches advanced topics on two real projects. Homework notebooks are well prepared, and don't cause frustration. I copied all learning materials an will revisit them at depth as I read suggested papers.

Great course.

by Nilesh G

Jun 16, 2020

The Great Course with practical life case studies on Chest xray and MRI image segmentation.

It will really helpful to explore another domain like Medical with implementation of AI.

Thanks Pranav for the Guidance throughout the course

by Philippe

Apr 24, 2020

Nice course. It was fun cool with the applied applications that ML can be used for in medicine. Really like how they focused a week on metrics because it's super important to think about that in order to properly evaluate models.

by Golnaz S

Oct 01, 2020

A great course that keeps the essential elements of a successful online course. It is really helpful for someone who has the ai knowledge but wants to get familiar with the concepts of medical image processing and analysis.

by RUDRA P D

Jul 02, 2020

The course consists of good assignments and concept clearing explanation. Just one problem which I faced is in the Week 3 assignment where the second last code cell didn't run due to kernel failure. Rest everything was good

by Naitik N S

Sep 18, 2020

One of the best courses I have ever come across, and the fact being they give you code to practice makes the concept learning more easier, and the instructor is awesome, thank you deeplearning.ai for this amazing course.