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.
ofrecido por
IBMAcerca de este Curso
48.200 vistas recientes
Fechas límite flexibles
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Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
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Nivel intermedio
Aprox. 11 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
Habilidades que obtendrás
Regression AnalysisSupervised LearningLinear RegressionRidge RegressionMachine Learning (ML) Algorithms
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Aprox. 11 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido 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 - Qué aprenderás en este curso
2 horas para completar
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 completar
9 videos (Total 77 minutos), 3 lecturas, 3 cuestionarios
4 horas para completar
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 completar
12 videos (Total 116 minutos), 3 lecturas, 4 cuestionarios
5 horas para completar
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 completar
11 videos (Total 126 minutos), 3 lecturas, 3 cuestionarios
Reseñas
Principales reseñas sobre SUPERVISED MACHINE 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 dic. de 2020
Learned really about supervised learning and more importantly regularization and some available methods.
por RP12 de abr. de 2021
I recommend this course to everyone who wants to excel in Machine Learning. This is a Great Course!
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