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.
Specialized Models: Time Series and Survival Analysis
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Specialized Models: Time Series and Survival Analysis
Instructors: Mark J Grover
15,771 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.5
(126 reviews)
Intermediate level
Some related experience required
11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
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Assessments
12 assignments
Taught in English
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This course is part of the IBM Machine Learning Professional Certificate
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There are 4 modules in this course
This module introduces the concept of forecasting and why Time Series Analysis is best suited for forecasting, compared to other regression models you might already know. You will learn the main components of a Time Series and how to use decomposition models to make accurate time series models.
What's included
10 videos3 readings3 assignments
This module introduces you to the concepts of stationarity and Time Series smoothing. Having a Time Series that is stationary is easy to model. You will learn how to identify and solve non-stationarity. Smoothing is relevant to you as it will help improve the accuracy of your models.
What's included
13 videos3 readings3 assignments
This module introduces moving average models, which are the main pillar of Time Series analysis. You will first learn the theory behind Autoregressive Models and gain some practice coding ARMA models. Then you will extend your knowledge to use SARMA and SARIMA models as well.
What's included
9 videos3 readings3 assignments
This module introduces two additional tools for forecasting: Deep Learning and Survival Analysis. In addition to AI and Machine Learning applications, Deep Learning is also used for forecasting. Survival Analysis is a branch of Statistics first ideated to analyze hazard functions and the expected time for an event such as mechanical failure or death to happen. Survival Analysis is still used widely in the pharmaceutical industry and also in other business scenarios with limited data related to censoring, the lack of information on whether an event occurred or not for a certain observation.
What's included
8 videos3 readings3 assignments1 peer review
Offered by
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