A lot of the code, did not work.

The tf.transform and Apache Beam concepts are not explained in simple ways.

Also the lab jumped from simple programs to complex programs.

The assessments do not actually require writing any code; you just execute the given code blocks. Little knowledge will be retained unless students actually write code and solve problems, even for the motivated ones who read through all the given code.

The videos are good and better than the last two courses in the specialization; however, the labs lack proper instructions and not that helpful. This course seems like more of an advertisement for Google Cloud Platform than feature engineering: details of engineering part is hardly covered in the course; more emphasis is on demonstrating on how to do it on GCP.

Would appreciate more guidance on the exercises

Had a lot issues with the quiz grader.

The notebook based demos are unfortunately pretty useless as labs. All of these courses would be much improved with real labs that require the student to build the system.

This course and the next course of the specialization is the most important course of the specialization. The reason is that other course except the first course deals with the working of APIs which might change in the near future but the insight that this couse provide on some of the topics is really really important, which I've not seen much discussed. This course is definitely a must-do.

The course provides an overview and details of a very varied, comprehensive, and advanced range of possibilities to do feature engineering. Because the software and API's presented have a lot of details you will have to work a lot more with the information provided to attain a "hands-on" feeling. However you get a good starting point and knowledge of the possibilities.

Great course. A bit more difficult than the other 3, because the topic is more complex. Once finished the course you'll get the big picture. It may take some time to digest all little details, but everything is very well explained in a more than exhaustive way. Teachers are also very engaging and never boring. Highly recommend to anyone interested in the topic!

Various enhancements in demonstrating a practical case in feature engineering, starting from ELT through training, evaluating, and lauching an ML engine, taught with a lot of enthusiasm. Recaps of relevant ideas in statistics, algebra and calculus we learned back in our school days (things that some of us "used to know") kind of helped.

This course should be mandatory for any ML practitioner. It teaches you that ML is not only about throwing whatever you want to (sort of) a model and expect to get reasonable results. It is about getting to know your problem and squeeze the data available.

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

A lot of great material that I have not seen covered other ML courses so far. My only complaint is that there is way too much material for a single week. It felt like it should be spread over two weeks at least.

It's a pretty interesting course, specially that's the only one that teaches featuring engineering with a focus on production issues, but it assumes some knowledge with apache beam, and dataflow.

i really like the effort taken in developing this course, the structure. Kudos to Laks for converting lots of statistical and coding language to very simple understandable english.

Feature engineering is important but less discussed compared to general ML or DNN. Feature cross is a new concept and yet very useful for dealing with large datasets.

The content is great, not just from a technical point of view but for all the know-how that the different instructors share during in the videos and labs.

Excellent Course and advice from experts about Feature Engineering and data pipelines utilizing advanced processes on GCP, thanks to Google and Coursera.

This module covers a lot of tricks that should be employed during preprocessing to improve the prediction accuracy of machine learning methods.

This course is a little bit harder than the former three courses. But the intuition of feature cross and embedding is very inspiring.

Very good stuff on feature engineering overall. Feature cross was new to me and looked very promising, and tf.transform was awesome.

very intresting and insightful ideas about data preprocessing and a great introduction to different tools to execute pipelines.

Amazing course! Gives insight into one of the most important part of solving Machine Learning problems; feature engineering!!

Detailed explanations and examples. At places the explanations and intertwined topics do get convoluted, but overall good.