This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
oferecido por
IBMInformações sobre o curso
40,506 visualizações recentes
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 11 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
Habilidades que você terá
Regression AnalysisSupervised LearningLinear RegressionRidge RegressionMachine Learning (ML) Algorithms
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 11 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
oferecido por
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.
Programa - O que você aprenderá com este curso
2 horas para concluir
Introduction to Supervised Machine Learning and Linear Regression
This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. After introducing the concept of regression, you will learn its best practices, as well as how to measure error and select the regression model that best suits your data.
2 horas para concluir
9 vídeos (Total 77 mín.), 3 leituras, 3 testes
4 horas para concluir
Data Splits and Cross Validation
There are a few best practices to avoid overfitting of your regression models. One of these best practices is splitting your data into training and test sets. Another alternative is to use cross validation. And a third alternative is to introduce polynomial features. This module walks you through the theoretical framework and a few hands-on examples of these best practices.
4 horas para concluir
12 vídeos (Total 116 mín.), 3 leituras, 4 testes
5 horas para concluir
Regression with Regularization Techniques: Ridge, LASSO, and Elastic Net
This module walks you through the theory and a few hands-on examples of regularization regressions including ridge, LASSO, and elastic net. You will realize the main pros and cons of these techniques, as well as their differences and similarities.
5 horas para concluir
11 vídeos (Total 126 mín.), 3 leituras, 3 testes
Avaliações
Principais avaliações do SUPERVISED LEARNING: REGRESSION
por NV15 de Nov de 2020
Very well designed course, great that we could work with our own data and apply the theory. Looking forward to continue the journey.
por AF6 de Nov de 2020
Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.
por NA27 de Dez de 2020
Learned really about supervised learning and more importantly regularization and some available methods.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
O que recebo ao me inscrever para este certificado?
Ganharei créditos universitários por concluir o curso?
Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.
