Specialized Models: Time Series and Survival Analysis
Master time series analysis and survival modeling for advanced machine learning forecasting techniques.
Master time series analysis and survival modeling for advanced machine learning forecasting techniques.
This course delves into specialized machine learning models for time series analysis and survival analysis. Students will learn forecasting techniques, including decomposition models, stationarity concepts, and time series smoothing. The curriculum covers ARMA, ARIMA, and SARIMA models, as well as deep learning approaches for forecasting. Additionally, the course introduces survival analysis for analyzing censored data and hazard functions. Through hands-on projects and best practices in statistical learning, learners will gain practical skills in implementing these advanced modeling techniques for real-world applications in various industries.
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What you'll learn
Identify common modeling challenges with time series data
Explain how to decompose time series data into trend, seasonality, and residuals
Understand and implement autoregressive, moving average, and ARIMA models
Select and apply appropriate time series models for various scenarios
Describe hazard and survival modeling approaches
Identify types of problems suitable for survival analysis
Skills you'll gain
This course includes:
6.3 Hours PreRecorded video
12 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 4 modules in this course
This course provides a comprehensive exploration of specialized machine learning models, focusing on time series analysis and survival analysis. Students will learn advanced forecasting techniques, including decomposition models, stationarity concepts, and time series smoothing. The curriculum covers ARMA, ARIMA, and SARIMA models, as well as deep learning approaches for forecasting. Additionally, the course introduces survival analysis for analyzing censored data and hazard functions. Through hands-on projects and best practices in statistical learning, learners will gain practical skills in implementing these advanced modeling techniques for real-world applications in various industries, such as finance, healthcare, and manufacturing.
Introduction to Time Series Analysis
Module 1 · 2 Hours to complete
Stationarity and Time Series Smoothing
Module 2 · 2 Hours to complete
ARMA and ARIMA Models
Module 3 · 2 Hours to complete
Deep Learning and Survival Analysis Forecasts
Module 4 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
Digital Content Delivery Lead at IBM with Extensive Experience in Information Technology Education
Mark J. Grover is a Digital Content Delivery Lead at IBM, specializing in the creation and delivery of online educational content. Before joining IBM, he was a full-time professor of computer technology at Cape Fear Community College in Wilmington, NC, where he coordinated the Information Security program and taught various courses including Computer Security and Network Administration. Grover has over 25 years of experience in information technology and has received accolades such as the Cisco Instructor of Excellence award and the Award for Excellence in Innovation from the University of North Carolina Wilmington. He is passionate about outdoor activities like camping and mountain biking, and enjoys spending time with his family.
Machine Learning Curriculum Developer at IBM Specializing in Data Analysis and AI Education
Miguel Maldonado is a Machine Learning Curriculum Developer at IBM, where he specializes in creating educational content focused on machine learning and data analysis. He teaches several courses on Coursera, including Deep Learning and Reinforcement Learning, Specialized Models: Time Series and Survival Analysis, Supervised Machine Learning: Classification, Supervised Machine Learning: Regression, and Unsupervised Machine Learning. Through his work, Miguel aims to equip learners with the essential skills needed to understand and apply various machine learning techniques across different domains, helping to bridge the gap between theoretical knowledge and practical application in the field of artificial intelligence.
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4.5 course rating
125 ratings
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