Structuring Machine Learning Projects

Loved the course, and the simulation was great. Doing an actual transfer learning programming exercise in TensorFlow could be an awesome addition.\n\nBest & thanks again for an awesome course!\n\nEric

I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

Going beyond the technical details, this part of the course goes into the high level view on how to direct your efforts in a ML project. Really enjoyable and useful. Thanks for making this available!

Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.

This is a must course in the entire specialization. It covers the step by step procedure to approach and solve a problem. The case studies provided are real world problems which are so much helpful.

This is the knowledge in which we will get from lots of experience only, but the andrew has shared in this course which might help us in future by saving a lot of time through this course experience

It's a great course! This course gave me a lot of new perspectives in constructing a machine learning project. Especially, the discussion of data distribution in the train/dev/test set is fantastic.

A lot of concrete examples, including those in the lectures and in the tests. Gained some thoughts on how to manage a ML project. Thanks Andrew and deeplearning.ai for providing such a great course.

Awesome course as always. The course teaches real world practical aspects of how to get started and navigate in the real world projects. The guidelines are actual learnings from years of experience.

Really a good course with mostly the theoretical knowledge on some aspects to reuse your model as well as some error analysis. Thoroughly taught with lots of real-life examples, thanks to Andrew Ng.

Interesting course especially for ML novices. A short course and could be completed quickly, however, one needs to carefully review the lectures to avoid missing key points. Well-structured course!

I am glad I took this class. There are a lot of things think about with respect to structuring your M/L project. Fortunately, it is not as mysterious as people often claim...but it is very nuanced.

Course is great although only in one case we have pdf's with additional lecture notes. They are more useful than ppt slides therefore it would be desirable to have them in other parts of the course

In this section, only strategies discussed and no programming practice. So I don't think I really understand those tricks. Maybe when I enroll in real projects, those magic will show their powers.

Videos could be shorter and better edited. Supporting materials could be better. Homework assignments were tedious and included rather confusing (and borderline tricky) questions and alternatives.

While there are lots of techniques out there, this course really helps you gain a different perspective on the bigger picture and teaches you to avoid some of the common pitfalls you may encounter

I recommend this course. This will be a bit of theoretical which is good. It will talk about real world scenarios over the errors which is what we deal in day-to-day life and how to deal with it.

It is a useful Content to keep in mind. Not as practical as other contents in the Specialisation. But, as Andrew Ng pointed out, it is a topic many times overlooked and I'm glad it was discussed.

Fascinating insights how to solve issues in ML projects. Saves lot of time if you have to find out yourself. Great share of experience and very appealing and practical examples, easily explained.

Amazing course contents and case studies to get a solid understanding of key Deep learning concepts and applications in real world scenarios...very helpful for freshers in Deep Learning like me.