Interesting course with a whole bunch of new algorithms! Although great work from the tutor in explaining all those slides and the codes, still sadly, I would again point out that the Accent is really really hard to comprehend, inspite of the fact that English is like my native language.

Secondly, in the latter half of the course, specially in the labs for Arima, Sarima, FB prophet etc. where there is a whole bunch of complex new information to be digested, the pace in the labs feels to be apparently very rushed and haphazard.

There are too many concepts presented together but in the end it remains still quite unclear the sequence in which these methods could be applied to solve real world problems.

Helpful would be to use more real world Data Sets than Toy sets and show the sequence in which all these different Algorithms could be applied together on the same data set, to compare their performances.

Nevertheless, owing to the complexity of the subject, I appreciate the hard work put in by the tutors and the team at coursera and IBM!

Thank you!

The problem with this course is they use simulated data which cannot cut it. They need to use real life datasets and students given chance on how to do it properly.

1/5 starts

TLDR: instructors do not explain how models work very well, just give ways to apply them

Notebooks are good material however the instructor does not do a good job at all ramping the explanation of model complexity from the lectures to the applications.

A major problem with this course is that the instructors promote a "black box" mentality, that is, do not explain to many lengths how the models work and gloss over many mathematical concepts and tell the users to just trust that it works and implement the API. I do not agree with this method of teaching is it cultivates a dangerous environment for data scientists/ analystics etc. To understand how to implement a model without having a high level understanding of the inner workings is not a practical approach and will lead to catastrophes when being rolled into production in industry.

I would encourage the instructors to fully audit the course material especially for the last 2 weeks of the course and provide more comprehensive material on the math behind the models rather than just referring to wikipedia pages.

This course was added to the Intro to ML certificate. The material is useful for a data analyst/ML practitioner, but the presentation is not at the level of the other courses. The introductory labs introduce the concepts of time series analysis well, with hands-on examples, but the discussion of AR, MA, and ARIMA models is muddled and the labs for these models are not well constructed (this is the only course in this series where I felt I had to go to other sources in order to understand some of the basic concepts) . The course would be improved with a more detailed walk thru of the steps in building ARIMA models (the Box-Jenkins criteria were not covered in lecture?). The prophet module and the DL lessons seem sort of tacked on -- I would have benefitted from more explanation of how to design a DL model to handle a time series analysis. Overall, I think this topic is a good addition to the corpus, but the specific design and presentation of the material is ineffective.

Not much details but good as an overview on the topic

Toughest of all the 6 courses in the bunch.

A very well-structured course with useful techniques and detail guidelines. The Python code templates are also really useful when bringing into real-life problems.

Good course with some useful tips, the Survival part of the course was particularly interesting.

The IBM Machine Learning Professional Certificate course is one of the complete course for someone familiar with python and wanting to learn different machine learning techniques. The final course of this professional certificate specialized models: time series and survival analysis is a good courses which introduces time-series based analysis such as ARMA, ARIMA and deep-learning models. The only downside of this course is that the discussion about survival analysis is too short. May be special-section can be provided as honors so that students can submit two homeworks i.e. for time-series and survival analysis.

I would highly recommend anyone who is wanting to learn about machine learning techniques and best practices to take this course.

Really great course to start and enhance your ML and Time series analysis. This course will touch base to all different aspects of Time series analysis. Also if you work on project work will help to acquire additional knowledge.

Excellenct course.

I could experience so many methodologies.

So tough to finish each project.

I really thank IBM and Coursera for this great course with just so small tuition fee.

It is a good course to build foundation on the modeling of timerseries data. It will be good to add other lessons for anomaly detection on timeseries.

This is an excellent course covering large areas of Time Series analysis and is a must for any one intending to learn the topics with some detail.

Clearly explaind. I am currently working on time series forecasting and predictions. This course helped me a lot about the details of the topics.

I liked this course. It gives all the necessary information about classical machine learning algorithms as well as deep learning techniques

Very Hands-On Course. Nice selection of covered topics....espacially the survival analysis part. I can highly recommend this course

this is one the great course i learned. both theoritical and practical went parrallely that made the course much more reliable.

The final project at the end is a really good idea. You just learn the most when you apply theory.

Really high level coding and intuition behind the whole idea of time series data. Thanks dear IBM.

Everything perfect, just content of 3rd week could have better examples or be more explained.