I wish there were more detail explanation about hyper-parameter tuning when we define NN Models.

other than that, this course was great and gave me lot of insights. Thank you.

I'd like to learn more about algorithmic principle（Although some Andrew‘s class link is attached. ）why not explain the principle combined with the real example

Wish there were graded programming exercises. The quizzes has questions not relevant to the goal of the lesson ex What is the seasonality of sunspots.

New techniques were learnt regarding how to create a time-series signal and how they can be manipulated for forcasting and feeding to DNN networks.

A good intro course to time series prediction. Would have loved some more data analysis and other time series methods like ARIMA and seasonal ARIMA

course is good but it dont get deeper on using things like multivariate time series, in addition the course practice materials where kind of lax

A little bit too simple cuz it only covers univariate time series practice. Would be better if there's more multivariate time series exercise.

I was really waiting to predict 100 data that was similar to sequence, that being said; this was a good introduction to time series analysis.

its a nice course but instead of using synthetic data, it would have been better if we use real-time datasets for our practice and learning.

The programming exercise should not have been optional. But overall, this was an amazing course. A thumbs up from my side. Thanks a lot.

A lot of typos and I felt that making the Graded Exercises instead of Non Graded is a much better experience for us the students.

Good and easy to follow course to learn tensorflow. You need a background on Machine Learning to fully benefit from this course.

It is nice in introduction to the use of TF in time series. But a bit difficult in the code which without detail explanation.

Great introductory course on time series and RNN. But it could benefit from including more depth on RNN and its mechanism.

I think this is a helpful introduction. It would have been helpful to delve into some multivariate sequence data examples.

It would be great if the projects at the end of each week were graded. They were easily solved using that week lesson.

This is the best course of the specialization. You learn about applying DNN, RNN, LSTM and CNN to time series data.

Assigned grades were too easy but is it very well made in order to understand how to build modes for time series

Great Course, enlights you were you should head. Although, it missed some further explanation of the steps used.

i think this course is better structured and more updated vs the IBM AI Engineering Professional Certificate.

Great course! It would have been nice to teach something about multivariate time series forecasting as well

Have to complete Andrew Ng's Deeplearning as this is very basic course on top of that for time series data.

add more videos to clear deep understanding of time series and more graded practical assignments too...