Retour à Sequences, Time Series and Prediction

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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....

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.

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.

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par Asad K

•Jul 31, 2019

The first week has some interesting discussion of time series data and some traditional non-ML methods for forecasting, but beyond that the course quickly divulges the all too familiar weaknesses of this specialization; lack of depth, elementary discussion, weak insight into common problems that arise during training models, and extremely poorly written quizzes that don't test the learner's gain of knowledge or skills in any meaningful way.

My biggest complaint to the instructors and the team is that for months this specialization promised the last course will discuss the WaveNet model, but the course didn't even do a cursory survey of it (In week 4, the instructor adds a Conv1D layer but doesn't even discuss the causal padding and completely skips dilations, etc, so that in effect there isn't even discussion of a single layer from WaveNet model). Sigh !

par Fengjun W

•Aug 18, 2019

Finally, wasted my weekend and 40 euros to finish this shitty specialization. I really dont know the target audience of this specialization. If you have no background of deep learning, going through some code snippets without any explanation wont help you at all. you can't know anything behind it. If you already have some knowledge, you will find nothing new and more in this course. 1) The materials are so shallow and without any depth, just reading the slides and codes with errors. Only some high-level keras APIs are covered. The official tensorflow tutorial is much better. 2) The test questions are of no value at all, it cant test any your understanding whether about deep learning or the tool tensorflow. The assignments are poorly designed, the answers contains errors. 3) I strongly doubt the instructor, I think he does not have much ML experience. Please don't waste your money and time on this specialization. If you want to learn deep learning, go to cs230; cs231n for computer vision; cs224n and cs224u for NLP; cs20 for Tensorflow.

par Irina G

•Aug 02, 2019

Very weak course, shallow, lacks content. Can be "learned" in a few hours, not weeks. Really hoped to see a working ML model for a time sequence, but the examples shown in this course do not demonstrate why bother with ML. If these examples were middle-school home work, they would be graded D+(keep trying or better use other methods). The instructor doesn't come across as an experienced ML practitioner.

par Steve H

•Aug 07, 2019

Very superficial presentation of the material, and disappointing content given all the initial hype. Whatever happened to working with WaveNet? The 4 weeks to complete the course is a massive over-estimate. Expect to spend not more than a day going through the course. Quiz questions are very low value and do not test any understanding.

par Kaan A

•Aug 24, 2019

Unfortunately, These whole Specialization didnt match my expectations. I finished whole Deep Learning Specialization and I LOVED IT. Before starting this one I had very good feeling about this specialization; however I learned very little. Most of the videos are like "this code does this and this code does this and this line does this and this function does this etc. " . A bit disappointed, but still learned some.

par Yaron K

•Sep 30, 2019

A step by step explanation of how to use TensorFlow 2.0 for building a Neural network for sequences and time series. With detailed examples of code and of how to choose hyper-parameters.

par Marghoob K

•Aug 03, 2019

This was really a beautifully designed course. They didn't focused on teaching too much of thing at once but build up the base slowly and strongly for better understanding.

par Parab N S

•Sep 14, 2019

An excellent course on Time Series and Sequences by Laurence Moroney. Explained how to use CNN, RNN and DNN together to bring the nest out of time series prediction.

par Subhadeep D

•Jul 31, 2019

Quite a good light-weighted course on Time Series and Prediction. It was quite helpful for people like me who are seeking ways to implement the concepts.

par Silviu M

•Sep 02, 2019

The material is great and the presentation elevated and professional. Few thoughts nonetheless: a) i know time series, came here for specific advice on how to tune models. I was extremely disappointed. Stationarity is mentioned at the very beginning but then it fades as if it was completely irrelevant to ML. b) there is more than one contradiction in the presentation. MAE is going up yet the presenter says that it got better??? That I think would be really confusing, particularly for novice learners. c) black boxes: I acknowledge that there are so many decisions and choices one needs to make when setting up a training model. Wouldn't it be relevant to highlight them and explain how different decisions impact the outcome? This course was failing on that.

par Jussi H

•Jan 07, 2020

I wanted to like this specialization, but I just cannot. My expectation was that this specialization would complement Andrew Ng's excellent Deep Learning specialization, but it does not. Whereas the DL specialization taught you best practices and a systematic approach to improving models, this specialization throws all of that out the window. The architectures are downright silly in some of the examples. If you want to learn TensorFlow, you would spend your time more wisely by working through the official TF tutorials, which are pretty good.

par Charlie C

•Dec 22, 2019

No concrete knowledge, no solid explanation. Just some demo.

par Alvaro M A N

•Jan 03, 2020

Personally I loved this course, I had a previous knowledge of this topic, because it's one of my favorites topics (very related to IoT analysis data). And here I've learned various top technics suchs lambda layers, or that we have to split in training, validation and testing periods the data. This is something that you don't see in many books or manual about time series with tensorflow. And finally I've learned very useful libraries that I even didn't know that exists like tf.keras.dataset, that makes so easy to give format to the data, before you had to write more code. So with this information I can write more effective and efficient code! Thanks Laurence and Andrew from Perú!

par Richard S

•Sep 15, 2019

This course was my ultimate motivator and goal for taking the specialization as I am doing work with time series. Very interesting to learn a traditional statistical approach, then apply DNNs, RNNs, LSTMs and CNNs to time series prediction. Even though just scratching the surface, I can apply knowledge from this course and specialization immediately.

Thank you Laurence and Andrew for a fantastic course and specialization! I am inspired and motivated to dig deeper into the theory of NNs and their application with further courses and projects.

par Saif H

•Aug 28, 2019

It was a brilliant course , I thoroughly enjoyed learning various aspects and techniques of Deep Learning techniques and in the process also learned a lot about TensorFlow . As mentioned by LM , its the first step and I'm really to have taken that first step.

One of the issue with the course has been the quality of audio, all the other course I have done on Coursera had very clear and audible voice over , however with this course I have struggled to hear with the audio, hopefully this can be addressed in future course.

par Hannan S

•Oct 28, 2019

First of all, the course was amazing! I found it great for the following reasons:

- Laurence Moroney (Instructor) was very professional and clear while delivering the knowledge

- The introductions by Andrew NG were really nice

- Easy to understand codes and understanding of thr underlying principles

- Varied topics such as CNN, NLP & Time Series

- Very insightful by providing expert opinions about different ways of model optimization

I really enjoyed the course and I thank the instructor for the same :)

par Michael

•Aug 17, 2019

I enjoyed the last course of the practice in tensorflow. There is a lot of note books to work with, the teaching was good and good referencing. Simple to understand, even though we might require more notes and also materials to work on the local jupyter notebook. Some simple code could be a night mare as you are using windows machine, linux, anaconda. As the courses progressed, there are more and more references to work with. Looking forward to the next set of courses.

par Andrés R

•Jan 26, 2020

Ok, this course was amazing, cause i pass a big large course in Udemy about Data Science for get a right way to complete my master degree tesis, and it was not enough for my, this course will help me to use my own data set that have been streamed for some sensors to analysed and predict them, before this course i don't know that CNN and LSTM is a right way to work with time series but, nowadays i know that is a good way, congrats Laurence and Andrew.

par Ravi P B

•Mar 15, 2020

Nice experience taking this course. Precise and to the point introduction of topics and a really nice way to start programming the models without going much into theory and a comprehensive and nice way to learn tensorflow framework. Mr. Laurence Moroney Sir has been excellent in all the courses and the conversations with Andrew sir are chilling as well as motivating. So its been a very good experience to take this specialization and learn tensorflow.

par Andrei N

•Sep 21, 2019

Very detailed step by step tutorials of using Tensorflow with lots of effort to make things as easy to understand as possible. Especially, examples of generation a time-series pattern simulations looks very thoughtful and helpful for the course topic. A little lack of theory comparing to other courses by deeplearning.ai. Quizzes are quite undeveloped. But that is understandable, because the main goal of the course to introduce Tensoflow.

par Robin R

•Dec 02, 2019

I needed wonderful course and wonderful code to make me understand the way to solve the 'Time Series' problem. My English skill is awful, but the professor explained so, so well that I could understand pretty well. I knew the concepts of the models before I take this course, but I didn't have any opportunity to see codes realized! Thank you very much for offering me such a nice lecture :)

par charles l

•Oct 23, 2019

This specialization was the ideal evolution in my DNN training after having taken Andrew Ng's classic ML course, followed by the deeplearning.ai DNN specialization. The instructor is excellent, as are the lecture notes, training materials, coding platform and examples! I am moving on to Advanced ML w/ TensorFlow on Google Cloud

par Wenlei Y

•Mar 19, 2020

I like all of the 4 courses in the entire series. Dr Moroney offers you the codes and you can play around by yourselves to better understand the concepts and the algorithms. My suggestion is: You can watch the videos and pass all the tests first and download the codes, and later you can study the codes in details.

par Anthony B

•Mar 15, 2020

At first, I thought this was tragically oversimplified. Then I realised that the real benefit of this course is the practical walkthroughs that it in the large consists of. Other courses can give you the theoretical foundations of Machine Learning, but this excels as a treasure trove of practical guidance.

par Ara B

•Sep 09, 2019

The real life example of using the TF was excellent. I like to see more utilization of the TF in real life, things like anomaly detection in human interaction or imaging interpretation in health industry or economical data and modeling in financial markets. It was an excellent course overall. Many thanks!

- L'IA pour tous
- Introduction à TensorFlow
- Réseau de neurones et deep learning
- Algorithmes, Partie 1
- Algorithmes, Partie 2
- Apprentissage automatique
- Apprentissage automatique avec Python
- Apprentissage automatique à l'aide de SAS Viya
- La programmation en R
- Intro à la programmation avec Matlab
- Analyse des données avec Python
- Principes de base d'AWS : Going Cloud Native
- Bases de Google Cloud Platform
- Ingénierie de la fiabilité du site
- Parler un anglais professionnel
- La science du bien-être
- Apprendre à apprendre
- Marchés financiers
- Tests d'hypothèses dans la santé publique
- Bases du leadership au quotidien

- Deep Learning
- Le Python pour tous
- Science des données
- Science des données appliquée avec Python
- Bases de la gestion d'entreprise
- Architecture avec Google Cloud Platform
- Ingénierie des données sur Google Cloud Platform
- Excel à MySQL
- Apprentissage automatique avancé
- Mathématiques pour l'apprentissage automatique
- Voiture autonome
- Révolutions Blockchains pour l'entreprise
- Business Analytics
- Compétences Excel pour l'entreprise
- Marketing numérique
- Analyse statistique avec R pour la santé publique
- Bases de l'immunologie
- Anatomie
- Gestion de l'innovation et du design thinking
- Bases de la psychologie positive