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Volver a Supervised Machine Learning: Regression

Opiniones y comentarios de aprendices correspondientes a Supervised Machine Learning: Regression por parte de Habilidades en redes de IBM

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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....

Principales reseñas

NV

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.

MK

11 de ago. de 2022

It was a great learning experience with in-depth knowledge and practice-based demos helped me to understand the concepts easily.

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