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.
Este curso faz parte do Certificado Profissional Aprendizado de máquina IBM
6 classificações
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Informações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 6 de 6 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 11 horas para completar
Inglês
Legendas: Inglês
Habilidades que você terá
Dimensionality ReductionUnsupervised LearningCluster AnalysisTime SeriesK Means Clustering
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 6 de 6 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 11 horas para completar
Inglês
Legendas: Inglês
oferecido por
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.
Programa - O que você aprenderá com este curso
2 horas para concluir
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.
2 horas para concluir
10 vídeos (Total 83 mín.), 3 leituras, 3 testes
10 videos
Introduction to Forecasting and Time Series Analysis11min
Pandas Time Series Notebook - Part 17min
Pandas Time Series Notebook - Part 210min
Pandas Time Series Notebook - Part 313min
Pandas Time Series Notebook - Part 49min
Time Series Decomposition4min
Decomposition Models9min
Decomposition Notebook - Part 17min
Decomposition Notebook - Part 27min
3 leituras
Time Series Demo (Activity)10min
Time Series Decomposition Demo (Activity)10min
Summary/Review10min
3 exercícios práticos
Check for Understanding5min
Check for Understanding5min
End of Module Quiz10min
3 horas para concluir
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.
3 horas para concluir
13 vídeos (Total 110 mín.), 3 leituras, 3 testes
13 videos
Stationarity Notebook - Part 17min
Stationarity Notebook - Part 27min
Stationarity Notebook - Part 314min
Nonstationarity Examples4min
Identifying Nonstationarity7min
Common Transformations6min
Time Series Smoothing6min
Smoothing Moving Averages5min
Smoothing Exponential Intro5min
Advanced Smoothing12min
Smoothing Notebook - Part 112min
Smoothing Notebook - Part 215min
3 leituras
Stationarity Demo (Activity)10min
Time Series Smoothing Demo (Activity)10min
Summary/Review10min
3 exercícios práticos
Check for Understanding5min
Check for Understanding5min
End of Module Quiz10min
3 horas para concluir
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.
3 horas para concluir
9 vídeos (Total 117 mín.), 3 leituras, 3 testes
9 videos
Useful Plots9min
ARMA Models Notebook - Part 110min
ARMA Models Notebook - Part 210min
ARIMA and SARIMA Models12min
SARIMA Prophet Notebook - Part 112min
SARIMA Prophet Notebook - Part 215min
SARIMA Prophet Notebook - Part 314min
SARIMA Prophet Notebook - Part 425min
3 leituras
ARMA Models Demo (Activity)10min
SARIMA Prophet Demo (Activity)10min
Summary/Review10min
3 exercícios práticos
Check for Understanding5min
Check for Understanding5min
End of Module Quiz10min
3 horas para concluir
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.
3 horas para concluir
8 vídeos (Total 70 mín.), 3 leituras, 4 testes
8 videos
Deep Learning - Part 26min
Deep Learning - Part 34min
Deep Learning Notebook - Part 114min
Deep Learning Notebook - Part 211min
Survival Analysis and Censoring - Part 110min
Survival Analysis and Censoring - Part 28min
Survival Analysis Notebook8min
3 leituras
Deep Learning for Forecasting Demo (Activity)10min
Survival Analysis Demo (Activity)10min
Summary/Review10min
3 exercícios práticos
Check for Understanding5min
Check for Understanding5min
End of Module Quiz10min
Sobre Certificado Profissional Aprendizado de máquina IBM
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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