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Voltar para Specialized Models: Time Series and Survival Analysis

Comentários e feedback de alunos de Specialized Models: Time Series and Survival Analysis da instituição IBM

4.5
estrelas
54 classificações
15 avaliações

Sobre o curso

This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices and verifying assumptions derived from Statistical Learning. By the end of this course you should be able to: Identify common modeling challenges with time series data Explain how to decompose Time Series data: trend, seasonality, and residuals Explain how autoregressive, moving average, and ARIMA models work Understand how to select and implement various Time Series models Describe hazard and survival modeling approaches Identify types of problems suitable for survival analysis Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics....

Melhores avaliações

MB
6 de Mai de 2021

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.

GS
15 de Mai de 2021

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.

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1 — 16 de 16 Avaliações para o Specialized Models: Time Series and Survival Analysis

por Ashish P

9 de Abr de 2021

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!

por Lam C V D

10 de Out de 2020

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.

por Mohamed G H

26 de Fev de 2021

Not much details but good as an overview on the topic

por Keyur U

24 de Dez de 2020

Toughest of all the 6 courses in the bunch.

por Rufus T

8 de Abr de 2021

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

por R W

26 de Jul de 2021

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.

por Mehul D S

1 de Jul de 2021

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.

por My B

7 de Mai de 2021

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.

por Ghada S

16 de Mai de 2021

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.

por george s

16 de Set de 2021

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

por Juan M

24 de Jul de 2021

Great course, very well taught and topics are useful for future applications

por Luis P S

17 de Jul de 2021

E​xcelente! Recomendable para iniciar en el mundo del Machine Learning.

por Jose M

16 de Fev de 2021

Again, thanks to the instructor in the videos

por Fernandes M R

19 de Jun de 2021

very good. It could be better, but it ok.

por vikas v

22 de Nov de 2020

Amazing Concepts explanations

por krysten z

16 de Out de 2020

not able to cancel the course.