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
IBM Skills NetworkInformações sobre o curso
90.470 visualizações recentes
Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
NÃvel intermediário
Aprox. 11 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
Habilidades que você terá
- Regression Analysis
- Supervised Learning
- Linear Regression
- Ridge Regression
- Machine Learning (ML) Algorithms
Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
NÃvel intermediário
Aprox. 11 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
oferecido por
IBM Skills Network
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 vÃdeos (Total 77 mÃn.), 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 vÃdeos (Total 116 mÃn.), 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 vÃdeos (Total 126 mÃn.), 3 leituras, 3 testes
Avaliações
- 5 stars79,78%
- 4 stars15,52%
- 3 stars3,24%
- 2 stars0,72%
- 1 star0,72%
Principais avaliações do SUPERVISED MACHINE LEARNING: REGRESSION
por PZ18 de abr de 2021
Very helpful course. There are few ups and downs but overall its helpful.
por NA27 de dez de 2020
Learned really about supervised learning and more importantly regularization and some available methods.
por UV5 de jul de 2022
awesome expirence and iam good to go towards an next course thankyou.
por MB14 de abr de 2021
A well structured course with useful techniques in real life.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
O que recebo ao me inscrever para este certificado?
Qual é a polÃtica de reembolso?
Mais dúvidas? Visite o Central de Ajuda ao estudante.
