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.
Offert par


Specialized Models: Time Series and Survival Analysis
Réseau de compétences IBMÀ propos de ce cours
Compétences que vous acquerrez
- Dimensionality Reduction
- Unsupervised Learning
- Cluster Analysis
- Time Series
- K Means Clustering
Offert par

Réseau de compétences IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Programme de cours : ce que vous apprendrez dans ce cours
Introduction to Time Series Analysis
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.
Stationarity and Time Series Smoothing
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.
ARMA and ARIMA Models
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.
Deep Learning and Survival Analysis Forecasts
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.
Avis
- 5 stars73Â %
- 4 stars14Â %
- 3 stars7Â %
- 2 stars4Â %
- 1 star2Â %
Meilleurs avis pour SPECIALIZED MODELS: TIME SERIES AND SURVIVAL ANALYSIS
excellent and well explained course, especially for SARIMAX models.
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.
Good course with some useful tips, the Survival part of the course was particularly interesting.
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.
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