Learner Reviews & Feedback for Structuring Machine Learning Projects by DeepLearning.AI

Some tips are very useful for practitioners but the same information is repeated over and over again that makes the course quite boring.

A small course with very effective tips and tricks to figure out how to start and proceed further while building a project effectively.

I think this lecture is very useful when we make our own ML system.

Also, it has many examples about errors we can usually meet in real.

Useful, practical material. I probably underappreciate the importance of someone (especially of Dr. Ng's stature) covering this for us.

Very practical lessons in this module that should make you and your team more efficient in implementing deep learning on real problems.

another great Andrew Ng course. This one gives practical insights in how to go about making your deep neural networks perform better.

I really found the pilot training quizzes are great and very helpful, but some questions one can debate if has the right answer or not

Quizzes could be refined since some of the questions are really confusing & need weird pre-requisite knowledge about human physiology.

It was an interesting course for sure, but it was a bit stretched and the notions explained could be compressed in a much shorter one.

I appreciate the review and hints on structuring ML projects. Just seemed a little lacking on the meat and potatoes of real practice.

Now I know what is Machine learning and its parts eg deep learning. The curse cleared the basic structure for machine learning to me.

Interesting to understand how to manage a problem during a ML project, really good trick and tip! Thanks Andrew and deep learning.ai!

It would be more useful to give explanation why an answer is correct and others are wrong, such details enhance our learning so much.

Machine Learning Simulator & course contents well prepares you to how a machine learning project should be structured and approached

Probably the least exciting of the five. This is a short course on how to approach machine learning projects, as the title suggests.

I thought this course was great content wise, but needs to improve on the errata in the content (repeated video sections), and quiz.

I think it should be useful but since I haven't got many practical experience, the course seems a little bit hard to catch up with.

Insight into how a model should be built and improved upon is really underrated. this course does emphasize on this, I enjoyed it.

It was a great course, but it lacks programming assignments and the quizzes were great (I really miss the programming assignments)

I think a little bit more programming homework can help me better understand the concepts, but other than that everything is good.

It's a good course to gain an initial understanding on the role that different real-world considerations play in Deep Learning NN.

Good course, covering interesting topics. Seemed too easy without enough content to make you feel like you mastered the subjects.

The course was very helpful. But I found some concepts to be repetitive. But it helped me to understand how to tackle the errors.

Very good insight on how to approach machine learning projects and the kind of things to look out for and tackle as they appear.

It gives guidance on how not to waste your team's time - quite valuable. There are some slight inclarities in the test questions.