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.
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课程信息
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第 6 门课程（共 6 门）
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中级
完成时间大约为11 小时
英语（English）
字幕：英语（English）
您将获得的技能
Dimensionality ReductionUnsupervised LearningCluster AnalysisTime SeriesK Means Clustering
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 6 门课程（共 6 门）
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为11 小时
英语（English）
字幕：英语（English）
提供方
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
教学大纲 - 您将从这门课程中学到什么
完成时间为 2 小时
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.
完成时间为 2 小时
10 个视频 （总计 83 分钟）, 3 个阅读材料, 3 个测验
10 个视频
Introduction to Forecasting and Time Series Analysis11分钟
Pandas Time Series Notebook - Part 17分钟
Pandas Time Series Notebook - Part 210分钟
Pandas Time Series Notebook - Part 313分钟
Pandas Time Series Notebook - Part 49分钟
Time Series Decomposition4分钟
Decomposition Models9分钟
Decomposition Notebook - Part 17分钟
Decomposition Notebook - Part 27分钟
3 个阅读材料
Time Series Demo (Activity)10分钟
Time Series Decomposition Demo (Activity)10分钟
Summary/Review10分钟
3 个练习
Check for Understanding5分钟
Check for Understanding5分钟
End of Module Quiz10分钟
完成时间为 3 小时
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.
完成时间为 3 小时
13 个视频 （总计 110 分钟）, 3 个阅读材料, 3 个测验
13 个视频
Stationarity Notebook - Part 17分钟
Stationarity Notebook - Part 27分钟
Stationarity Notebook - Part 314分钟
Nonstationarity Examples4分钟
Identifying Nonstationarity7分钟
Common Transformations6分钟
Time Series Smoothing6分钟
Smoothing Moving Averages5分钟
Smoothing Exponential Intro5分钟
Advanced Smoothing12分钟
Smoothing Notebook - Part 112分钟
Smoothing Notebook - Part 215分钟
3 个阅读材料
Stationarity Demo (Activity)10分钟
Time Series Smoothing Demo (Activity)10分钟
Summary/Review10分钟
3 个练习
Check for Understanding5分钟
Check for Understanding5分钟
End of Module Quiz10分钟
完成时间为 3 小时
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.
完成时间为 3 小时
9 个视频 （总计 117 分钟）, 3 个阅读材料, 3 个测验
9 个视频
Useful Plots9分钟
ARMA Models Notebook - Part 110分钟
ARMA Models Notebook - Part 210分钟
ARIMA and SARIMA Models12分钟
SARIMA Prophet Notebook - Part 112分钟
SARIMA Prophet Notebook - Part 215分钟
SARIMA Prophet Notebook - Part 314分钟
SARIMA Prophet Notebook - Part 425分钟
3 个阅读材料
ARMA Models Demo (Activity)10分钟
SARIMA Prophet Demo (Activity)10分钟
Summary/Review10分钟
3 个练习
Check for Understanding5分钟
Check for Understanding5分钟
End of Module Quiz10分钟
完成时间为 3 小时
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.
完成时间为 3 小时
8 个视频 （总计 70 分钟）, 3 个阅读材料, 4 个测验
8 个视频
Deep Learning - Part 26分钟
Deep Learning - Part 34分钟
Deep Learning Notebook - Part 114分钟
Deep Learning Notebook - Part 211分钟
Survival Analysis and Censoring - Part 110分钟
Survival Analysis and Censoring - Part 28分钟
Survival Analysis Notebook8分钟
3 个阅读材料
Deep Learning for Forecasting Demo (Activity)10分钟
Survival Analysis Demo (Activity)10分钟
Summary/Review10分钟
3 个练习
Check for Understanding5分钟
Check for Understanding5分钟
End of Module Quiz10分钟
关于 IBM Machine Learning 专业证书
Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis.
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