This course demystified the dev/test sets and gave me the strategies on how to select and troubleshoot the dev & test sets, how to do error analysis and how to chose appropriate actions.

I really learn allot abot practical aspects of machine learning I recommend any one who want to learn the waht machine learning engineer face problems while training and evaluating model

Teaches important knowledge about structuring projects that other ML engineers may take months or years to learn, provides 'flight simulator' problem sets for testing this new knowledge.

This course gives good insights about how to approach a ML problem and what are the key elements to look at in order to have an initial analysis of the results. Definitively recommended

El sello de calidad de los cursos de Andrew Ng, un curso de tomar bastante rápido pero con mucho conocimiento transmitido a través de los años de experiencia de los expertos en el área.

It is very good because learning is one thing but apply learning in different application is totally different thing, so this help anybody to improve their performance in deep learning.

The quiz based on real-life application scenarios gave an insight to the problems faced in practical deep learning applications. This helps a learner to look at things from many angles.

This course is more about the higher level strategies to best apply deep learning in practice. Other courses focus more on the math of neural networks and implementing them as software.

This course provides practical ideas and insight for constructing machine learning or deep learning projects. Concrete examples are given to help explain the concepts or real scenarios.

Much needed advice missing elsewhere. It will surely help systematize the learning/modeling process which is time-consuming and based on trial and error. The material is golden. Thanks!

It's very unique course i have ever taken on machine learning. It teaches you the best practices in ML projects. These could be really helpful when you are stuck on your ML project. I

The material taught in this class is rarely taught in a usual classroom yet it is very useful in practice. I highly suggest people who are interested in doing AI learn from this course.

Great course! Andrew Ng brings everything together in this third course of the series. This will help me make good design decisions and I feel that I have learned from his experience.

This course has been much more challenging than expected, even when it doesn't talk much about algorithms or math, the content is actually complex and develops great analytical skills.

It's an absolutely essential course for all DL wanna-bees. Provides deep and practical insights into solving complex DL projects that otherwise will take years to experience to master.

Really interesting, and important topics, that are hard to find elsewhere. Has given me a great insight into issues seen in deep learning projects and many areas of things to think of.

Knowing what transfer learning is, multitask learning and what is end to end deep learning was really fascinating. I also learnt about errors to consider which helps saving time later.

This course is excellent for its content. A few of the quiz questions are difficult to parse, which I have also seen in the other courses I have done on deep learning by Prof Ng et al.

Loved the flight simulator styled quizzes.. learnt a lot on how we can take a systematic approach towards building a right model and how to deal with challenges with data availability.

It is probably the best course out of first 3. The approach to week's tests really forces you to think hard. It does look like a very good way to transfer the experience! Thanks a lot.

I appreciated the high level guidance that this course offered. It is so easy for me to get bogged down the the details of a technical project and lose sight of the overarching goals.

The course was nice, and though I initially felt it a bit over-extended with just examples, I find that it helps to gain a deeper insight into structuring the machine learning models.

Interesting learning different metrics which can be used to identify problems in Machine Learning Projects in order to avoid wasting large amounts of time and energy on wrong metrics.

A very useful and intuitive course for understanding how to improve a model for different projects and situations. Once again, many thanks to Prof. Andrew Ng for building this course.

Found this course very relevant to me as a newbie. I think following the guidelines mentioned in the course will avoid lot of common mistakes made by many freshers in the field of DL.