Zurück zu Sequences, Time Series and Prediction

Sterne

4,455 Bewertungen

•

704 Bewertungen

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....

OR

3. Aug. 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.

MI

6. Juni 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.

Filtern nach:

von José D

•26. Apr. 2020

In this final course of the Tensor InPractice Specialization, all pieces come together to solve a real world example (Kaggles' sunspots) using Keras (TensorFlow's high-level API). This course focuses on Time sequence, using CNN & RNN. As explained in the videos, this specialization is an introduction to Deep Learning using Keras. There is no math in all of the courses. As a result, if you want to understand why and how it works under the hood, you want to do the "Deep Learning" Specialization. As I did The DeepLearning one before this one, this whole specialization was like an addition exercise.

von Francisco F

•23. Sep. 2020

I wish this course was taught with real world data, which only happens in week 4. Rather, the course utilizes synthetic data, which is not as great in providing perspective as real data and real problems. Also, volume is really low, don't know why. Other than that, as always, great course. Great specialization! A pretty good intro to Tensorflow for the ones who haven't used it before and a nice recap of the basics for the ones like me who have been using and have missed some core concepts here and there.

von Muthiah A

•9. Jan. 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

von João A J d S

•3. Aug. 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.

von Edward T

•8. Aug. 2020

Its a shame that the assignments were not graded. What's the incentive to struggle and dive deep when the notebook is just a repetition of the lecture notebooks and the assignment is ungraded? This course would greatly benefit in making those assignments graded and bringing in multivariate, multistep forecasting into the mix! Overall, though, I did enjoy it and I learned a thing or two about modelling and signal processing. Need to continue on this journey!

von Mihail Y

•10. Sep. 2020

Very interesting course. Thanks to Laurance Moroney for the clear and concise way of presenting complex concepts. The only reason I am giving 4 star to the course is that I was really expecting to get more about the information on forecasting using multiple series as features and forecasting multiple series at once. I think it will be interesting to add to the course more on this, as it is still tricky for me to manage these tasks in Tensorflow.

von Shubham K

•18. Aug. 2020

Really nice introduction time series data analysis for regression and prediction. This course extends what you will learn in the rest of the specialisation (NN, Dense Layers, Convolutions, RNN, LSTM) to univariate time series data. I highly recommend this. Its very easy after you do rest of courses from specialisation. Good luck learning. And kudos to Laurence and Dr Andrew Ng for being a lovely instructors and making this accessible to all .

von Suhan A

•7. Juni 2020

I liked the flow of the course, working on synthetic data and then moving to real data. But I also think it would be better if I had already taken Andrew Ng's Deep Learning Course before approaching this Course. Plus since there weren't any Graded Programming exercise, it didn't feel like I would be confident in making my own model. So I'm going back and taking Deep learning course.

von Tibor S

•3. Jan. 2021

Great course for a brief intorduction to time series predictions. One needs to integrate knowledge gained from somewhere else (i.e. the course is not comprehensive, but that is also not expected). What I was missing is clarification from authors of some of the important questions/comments in the forum. Several things from the course are left unexplained. Otherwise, I recommend it!

von Gerard S

•26. März 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

von Dustin Z

•27. Juni 2020

Fun course, like the rest in the series. I hadn't seen neural networks applied to time series, so that was really worthwhile to learn.

There are still some rough edges and a few parts of the labs that aren't addressed in the videos.

I really enjoy the format of the courses which emphasized a lot of experimentation with networks and provided opportunity for trial and error.

von Eric L

•11. Dez. 2020

If one has seen LSTMs from the previous course and has been exposed to time series there is a little conceptual material to learn from this course, but of course the focus is on tensorflow/keras programming. Highlights were learning how to include lambda layers (which allow one to execute arbitrary code in the network) and how to automate selection of the learning rate.

von Vladimir K

•26. Apr. 2021

Very nice explanations and videos, so I really like it. Basic for timeseries covered and a lot of detailed explanation provided how to apply TF to forecasting. However, quizes are too trivial and doesn't measure understanding of material IMHO. It will be great if separate data set will be used to control understanding of material and motivate to practice more with TF.

von yuan j

•16. Aug. 2020

I learned some time series models and processing methods, but I think this course is too simple and too shallow, for example, there is no prediction of the situation that contains complex features, and how to combine autoregressive features with other features. Models in this course are very popular and are used by everyone, there is no deep stuff

von Yusuf F M

•13. Juni 2020

A really helpful course for those who have just started their journey in the field of machine learning and AI. Strongly recommended for gaining great insights within the field, though all the materials covered are quiet shallow and practical. Use this as a way of learning the tools, not for mastering the theoretical background behind it.

von Arslan G

•28. März 2021

I really wish you could extend the course to multivariate time series prediction, as well as into multivariate time-series multiclass classification. That was indeed the reason why I wanted to learn more about sequence models, as I will be using them in my research on decoding EEG signal. apart from that, it's an excellent course

von Abhinandan T N

•26. Apr. 2020

Sunspot example was good, but i preferred to have few more examples to exhibit different types of real data, like data having both seasonal and also trend. Though the concept is well explained using synthetic data confidence on the subject would have more if had real data for all different types.

von Amir H

•14. Dez. 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.

von Vaibhav v s

•22. Juli 2020

The awesome learning experience with Coursera. So far, I have completed up to the Deep learning specialization. All the courses are well structured with self-learning, live quiz, and assessment. The trainers are good, connect to students, and answer questions. Happy learning.

von FERNANDO D H S

•10. Juli 2020

Good course, I'd liked more the evaluation methodology of the first two courses on the specialization: questionare and coding excercises. Although here we have ungraded excercises it is more rewarding to see that effor translated to the grades.

Thanks again and great courses.

von Roghaiyeh S

•2. Aug. 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.

von Sharad C R

•30. Dez. 2020

This course enables you to start using TensorFlow as an off the shelf tool. The idea of this course is to make you comfortable with using TensorFlow for predicting time series data. Theory and statistics behind dealing with such data is beyond the scope of this course.

von Amarendra M

•6. Sep. 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 :)

von Alfonso C

•20. Sep. 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! ;-)

von Raphy B

•30. Apr. 2021

The exercises are not so well constructed compared to the other courses in this specialization. Overall, the content is "spot-on" (pun intended) when it comes to explaining time-series and what methods we can use to approach to these problems.

- Google Data Analyst
- Google-Projektmanagement
- Google-UX-Design
- Google IT-Support
- IBM Datenverarbeitung
- IBM Data Analyst
- IBM-Datenanalyse mit Excel und R
- IBM Cybersecurity Analyst
- IBM Data Engineering
- IBM Full Stack-Cloudentwickler
- Facebook Social Media Marketing
- Facebook Marketinganalyse
- Salesforce Sales Development Representative
- Sales Operations in Salesforce
- Buchhaltung mit Intuit
- Vorbereitung auf die Google Cloud-Zertifizierung: Cloud Architect
- Vorbereitung auf die Google Cloud-Zertifizierung: Cloud Data Engineer
- Eine Karriere starten
- Auf eine Zertifizierung vorbereiten
- Bringen Sie Ihre Karriere voran

- Kostenlose Kurse
- Lernen Sie eine Sprache
- Python
- Java
- Webdesign
- SQL
- Gratiskurse
- Microsoft Excel
- Projektmanagement
- Cybersicherheit
- Personalwesen
- Kostenlose Kurse in Datenverarbeitung
- Englisch sprechen
- Inhalte verfassen
- Full-Stack-Webentwicklung
- Künstliche Intelligenz
- C-Programmierung
- Kommunikationsfähigkeiten
- Blockchain
- Alle Kurse anzeigen

- Kompetenzen für Datenwissenschaftsteams
- Datengestützte Entscheidungsfindung
- Kompetenzen im Bereich Software Engineering
- Soft Skills für Ingenieurteams
- Management-Kompetenzen
- Marketing-Kompetenzen
- Kompetenzen für Vertriebsteams
- Produktmanager-Kompetenzen
- Kompetenzen im Bereich Finanzen
- Beliebte Kurse in Datenverarbeitung im Vereinigten Königreich
- Beliebte Technologiekurse in Deutschland
- Beliebte Zertifizierungen für Cybersicherheit
- Beliebte IT-Zertifizierungen
- Beliebte SQL-Zertifizierungen
- Karriereleitfaden für Marketing-Manager
- Karriereleitfaden für Projektmanager
- Python-Programmierkenntnisse
- Karriereleitfaden für Webentwickler
- Datenanalysefähigkeiten
- Kompetenzen für UX-Designer

- MasterTrack® Certificates
- Zertifikate über berufliche Qualifikation
- Universitätszertifikate
- MBA- und Business-Abschlüsse
- Abschlüsse in Data Science
- Abschlüsse in Informatik
- Abschlüsse in Datenanalyse
- Abschlüsse im Gesundheitswesen
- Abschlüsse in Sozialwissenschaften
- Management-Abschlüsse
- Abschlüsse von europäischen Spitzenuniversitäten
- Masterabschlüsse
- Bachelorabschlüsse
- Studiengänge mit Performance Pathway
- BSc-Kurse
- Was ist ein Bachelorabschluss?
- Wie lange dauert ein Masterstudium?
- Lohnt sich ein Online-MBA?
- 7 Finanzierungsmöglichkeiten für die Graduate School
- Alle Zertifikate anzeigen