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Voltar para Supervised Machine Learning: Regression

Comentários e feedback de alunos de Supervised Machine Learning: Regression da instituição IBM

245 classificações
51 avaliações

Sobre o curso

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. By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning  Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Supervised Machine Learning Regression techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics....

Melhores avaliações


15 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.


6 de nov de 2020

Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.

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51 — 54 de 54 Avaliações para o Supervised Machine Learning: Regression

por Juhi S

20 de mai de 2022


por YASH A

22 de abr de 2021


por Ramesh B

30 de jan de 2021

The course is incomplete on regression analysis. Also, the grading scale was biased after putting in a lot of time and effort(20 pages). The reason was I didn't follow the assignment questions.

por Weishi W

6 de fev de 2022

It is actually disguisting course. Simply reading the powerpoint without any clear explanation. So bad