Back to Sequences, Time Series and Prediction

4.6

stars

1,367 ratings

•

222 reviews

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....

Mar 22, 2020

Really like the focus on practical application and demonstrating the latest capability of TensorFlow. As mentioned in the course, it is a great compliment to Andrew Ng's Deep Learning Specialization.

Aug 04, 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.

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By Rodrigo R N

•Sep 24, 2019

Show!

By 李英斌

•Sep 18, 2019

nice！

By Jurassic

•Sep 06, 2019

good

By echo

•Aug 31, 2019

good

By 林韋銘

•Sep 11, 2019

gj

By Egor E

•Aug 24, 2019

I like very match the first and second week of the course, because it contains dense new theoretical and practical things. The idea of time series forecasting and preparing windowed dataset was explained very clear and was very usefull for all next lessons. Also the difference between statistic and neural network approaches was very helpful.

The 3 and 4 week I would prefer zip in one , because the experiments with RNN, LSTM and Conv is very familiar and actually I've done them together one by one. I would pleased to learn some explanation and examples why each type of architecture follow their result. How the results depend on dataset preparation. Particulary, I did not get what architecture work better with seasonality, autocorrelations, and noise.

By Xiang J

•Oct 07, 2019

I think overall it is a good course, these are the things I learnt:

First-hand experience with tensorflow, but more focus on the basics of keras

Knows how to preprocess data for image, text, and times series to feed it into NN

Knows basic concepts of keras layers such as CNN, LSTM, RNN, Conv1D, DNN

Knows learning rate rough gauge techniques

Things to improve:

Fix the typos, such as window[:1], there are a few posted in the forum

Should introduce more basics of tensorflow instead of kerasShould

include more links/documentation for the side knowledge, such as paddingAdding

some layers seems magical, such as Conv1D before LSTM for time series, what is the logic behind?

By Muthiah A

•Jan 09, 2020

I enjoyed the thoughtful exercises and measured experienced guidance of Laurence (who has been doing this for years now in big stage). It’s a bite sized introduction to Tensorflow aspects for busy professionals and while you can “game” the quizzes and earn completion, really the onus is on learner to spend time on reading materials and videos and great colab exercises. Google Colab notebooks are single outstanding reason this whole specialization is compelling to me.

Thanks everyone @ Coursera

By João A J d S

•Aug 03, 2019

I think I might say this for every course of this specialisation:

Great content all around!

It has some great colab examples explaining how to put these models into action on TensorFlow, which I'm know I'm going to revisit time and again.

There's only one thing that I think it might not be quite so good: the evaluation of the course. There isn't one, apart from the quizes. A bit more evaluation steps, as per in Andrew's Deep Learning Specialisation, would require more commitment from students.

By Gerard S S

•Mar 26, 2020

First of all congratulations on the specialization. I felt that I have improved a lot my previous knowledge of Machine Learning and programming with Python and TS. One improving note:I felt that this course could go to third place in the specialization. You go deeper in CNN and LSTM which I missed in the previous one :)

Also, it would be great 2 examples of real-world scenarios

By Amir H

•Dec 14, 2019

Thank you for this very interesting and informative course. I really enjoyed the simplicity in explanation and the hands-on implementations. One thing that I think will improve this course further is to add more intuition and explanation of using particular structures like CNN followed by LSTM.

By Eli S

•Aug 02, 2019

I was looking for a basic step by step guide to Tensorflow and this course was amazing. I can now use my knowledge in DL from Deep Learning course better. The instructor was great, explained everything clearly. I think it was better if there was programming assignments too.

By Amarendra M

•Sep 06, 2019

I think this course will be of great help if one has worked on time series data. I was a complete novice to time series, and found it difficult to relate. However, I learnt a great deal about the tensorflow technical aspects.

Thanks Lawrence for making it so easy :)

By Alfonso C

•Sep 20, 2019

The course is great, but I would have loved knowing more about how to deal with multivariate time-series, data sets with many time-series, variable prediction horizon etc.

Hope a more advanced course on time series forecast with tf.keras is under construction! ;-)

By Yingnan X

•Oct 28, 2019

The homework exercise seems to heavily overlap with the demo notebook that I can simply copy and paste the code into the exercise notebook. It would be great if in the future the exercise can be a little harder and involve more thinking.

By Shiladitya P

•Mar 19, 2020

I learned the best practices for forecasting using statistical techniques as well as deep learning networks in this course. One point for improvement is to focus on a few multi-variate examples with code, which was absent in the course.

By Александр З

•Oct 01, 2019

I would like to have more info on window and batch sizes - seems to be pretty important values to work with, but they are not covered in depth.

In general, greate course that shows how to prepare sequences, feed them in to NN.

Loved it.

By Vahid N

•Jan 19, 2020

It is very easy to follow this course. I wish some function/object options and arguments (such as why we use Y^hat (hat is usually reserved for estimated values) and not Y in LSTMs) were explained in more detail for curious readers.

By mehryar m

•Dec 27, 2019

I'm so glad to take this course and build my knowledge regarding time-series data and modern approaches to create prognostic models. Thanks to Andrew Ng and L. Moroney to provide this course.

By Siddhartha P

•Mar 27, 2020

Few hands on programming assignments could be better for experience as was the case with starting two courses. Overall good course and the structure was well laid. Thanks for building it up

By William G

•Aug 16, 2019

Though I feel some aspects of this course did not delve deep enough into the explanations of some functions, the course helped me understand how to use models for time series problems.

By CM

•Aug 19, 2019

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.

By Parth A

•Aug 11, 2019

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

By Jesse S

•Aug 12, 2019

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

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