Chevron Left
Back to Sequences, Time Series and Prediction

Learner Reviews & Feedback for Sequences, Time Series and Prediction by DeepLearning.AI

4.7
stars
3,834 ratings
616 reviews

About the Course

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization....

Top reviews

MI
Jun 6, 2020

I really enjoyed this course, especially because it combines all different components (DNN, CONV-NET, and RNN) together in one application. I look forward to taking more courses from deeplearning.ai.

OR
Aug 3, 2019

It was an amazing experience to learn from such great experts in the field and get a complete understanding of all the concepts involved and also get thorough understanding of the programming skills.

Filter by:

551 - 575 of 617 Reviews for Sequences, Time Series and Prediction

By Jiawei X

Jan 11, 2020

This course is great for introduction. BUT it is still lacking very important background information of the Tensorflow Dataset and how to master it.

It makes sense not to go into too deep on the NN model and their theories but when it comes to practical usage of any machine learning packages, data pipelines play very significant role (count towards 60% - 70% of the codes).

In the course we briefly talk about Dataset and use only a few APIs without explaining why and the logic behind them. And tutorials from tensorflow's officials still lacking useful guidelines when dealing with dataset of multiple dimensions.

By Yemi A

Aug 16, 2019

I found the start of the specialism was very well explained; and as a result now I really understand CNNs (as it is was explained much better than the other courses I’m doing on Udemy and LinkedIn Learning). However I would suggest that Andrew and Laurence revisit the latter part of the course from a learner point of view, looking at the pain points along their journey through Sequences and Predictions. Overall, the structure of the whole specialism can be improved, and I find it not as good as my previous course (Andrew’s Standford University Machine Learning Course which was brilliant)

By Egemen Y K

Jun 4, 2020

Though the course is very educational, the prediction is done at the right way. One can not use the windows of validation data to test it. The testing accuracy should be measured via point by point prediction which predicts the future value based on the predictions. At that way, the hardness of the problem makes sense, otherwise anyone could use the linear regression models rather than LSTMs. Please review the content again since it requires lots of stuff that is not covered like multivariate analysis, sequence prediction as well as point b ypoint prediction.

By Ethan V

Sep 6, 2019

This is a good introduction to the API of keras, but that's not what I would expect from a "Tensorflow In Practice" Specialization. This is really an "Introduction to Keras" specialization, and really theory light one as well. As a graduate of the Deep Learning specialization, I expect this to be a way to apply that theory to large datasets and to novel architectures requiring some leverage of the lower level tensorflow APIs. Although I thought this course was well made, I feel it was not ambitious enough for it's name.

By Miguel L

May 27, 2020

I would leave 5 stars for the instructor. But the support you get from the forum sin minimal. There are tons of recurrent, important posts and threads unanswered...some of them even have months old. I may have posted or upvoted ten different questions and maybe received answers for three...and from fellow students who may or not may be right. That could really seem like a good place to start looking at some improvements. Not to mention the constant workarounds you have to do to successfully submit assignments.

By Justin F

Dec 28, 2020

I echo some of the comments of others. The code needs to be more commented with explanations. There were details in the code that were not mentioned in the lectures or explained. When someone does not understand a particular line, then it is difficult to understand the rest of the code. The Deep Learning Specialization was much more complicated than this specialization, but I understood it better because it covered more of the details with clarity. Much of the code in this course had no comments at all.

By Ed E

Dec 9, 2020

Too much focus on creating synthetic data and arbitrary code. Unlike the first three courses this was hard to follow with significant gaps in the material not explained.

Although I passed I am still unsure of what I have learnt on this course.

My advice would have been to use a real dataset from the start and build on this and eliminate all the helper functions that just really proved a distraction. This would also be a great motivator if the dataset was interesting.

By Pablo A

Sep 24, 2020

Just like Course 3, Course 4 was a let down. The content is interesting but I think unlike Courses 1 and 2 it is presented in a way that is kind of plain and not really all that engaging. I also think the assignments should still be required as this adds incentive to really work hard at it. I learned a decent amount, but Courses 3 and 4 of this specialization were a disappointment.

By Yarik M R

Feb 23, 2021

The materials are outdated and they are not as described as the first 2 courses (the effort and quality to make the curse is not the same as the others). The notebook from the first courses are very well documented and the ones from the last two are just code. Other than that the curse is great and well explained

By Chip J

Mar 21, 2020

Much preferred the Andrew Ng courses where we spent time coding specific sections of various neural nets. This ourse was practical, I guess, focusing on the mechanics of prepping data, but I don't feel it helped my understanding of the various machine learning techniques at all.

By Mushfiqur R

May 14, 2020

Some of the topics could have been described in details. There was always some kind of rush going on. By the way, I have come across several datasets and those labs introduced me to various neural network and their application using Tensorflow and Keras. Thanks to Laurence.

By Kevin H

Jul 12, 2020

Graded, non-optional assignments should really be added to this, and the rest of the courses.

It would help ensure the understanding of the tools in question. Providing the answers as Colabs is nice and helpful, but does not drive you to actually try things out.

By Haoyu R

Nov 27, 2019

In last week, the course gets really worse. The code are not well explained. And no tutor is there for answering the questions. For example, suddenly change the model from sequences2vector to sequences2sequences without any notification. What a shame.

By Alejandro B G

Sep 4, 2019

Teacher is not anywhere close to Andrew, plus the grading tools are non-existent. It goes too heavy on preprocessing when we want to learn tensorflow, you could've spent all that time in teaching Tensorflow appart from Keras.

By Eugene Y

Oct 22, 2020

Barely scratched the surface of the topic. For this particular course (alongside NLP too), I constantly had to look for more sources of information as certain aspects of the code implementation was not thoroughly explained.

By Hector B

Jun 12, 2020

Compared to the first two courses in the specialization, the last two lack a lot of practical coding homework, and there is really where the concepts are fixed. The course should have more graded coding excercises.

By Asad M

Feb 23, 2020

It's a relatively shallow course. They don't really dive down to the details or even don't cover whole a lot when it comes to examples, exercises or assignments. So, This is very much for the beginners.

By Igors K

Nov 23, 2019

The week 2 quiz is super bad (even pro NN people didn't couldn't answer some questions cos they're badly worded).

I honestly am not a fan of getting the notebook after the video that explains it.

By peropop

Feb 4, 2020

Nice course. Despite it's a practical one, you should consider to get just some deeper in the theory embedding the models you presented, to make the audience understand better what's going on.

By Slav K

Nov 12, 2019

As from fulltime ML practitioner and occasional user of TF this course is not too much in "practice" as many things are left untouched. However can be beneficial to complete newbie.

By Dimitry I

Aug 26, 2019

Good course, but seems a bit hastily put together. I really liked the technique for determining optimal learning rate. Thank you Mr. Moroney and the entire Coursera team.

By J N B P

Feb 3, 2021

This course teaches how to use the tools in Tensorflow for predictive analysis. It would be more helpful if you're familiar with sequence models.

By Harshin N

Aug 16, 2020

Course should more focus on real time data set, and assignments should be based on building the model, rather than this kind of simple questions.

By Bogdan L

Jul 7, 2020

This course was less detailed than I had hoped. I feel I have learnt more from the CNN and NLP courses in this specialisation.

By Ghifari A F

May 14, 2020

I didn't expect the course would be this easy and simple. But overall, this course is useful for introduction to TensorFlow.