Acerca de este Curso

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Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 11 horas para completar
Inglés (English)

Habilidades que obtendrás

Decision TreeEnsemble LearningClassification AlgorithmsSupervised LearningMachine Learning (ML) Algorithms
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 11 horas para completar
Inglés (English)

ofrecido por

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IBM

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

2 horas para completar

Logistic Regression

2 horas para completar
10 videos (Total 91 minutos), 6 lecturas, 3 cuestionarios
10 videos
Optional: How to create a project in IBM Watson Studio5m
Introduction: What is Classification?6m
Introduction to Logistic Regression2m
Classification with Logistic Regression12m
Confusion Matrix, Accuracy, Specificity, Precision, and Recall7m
Classification Error Metrics: ROC and Precision-Recall Curves10m
Logistic Regression Lab - Part 113m
Logistic Regression Lab - Part 216m
Logistic Regression Lab - Part 313m
6 lecturas
About this course3m
Optional: Introduction to IBM Watson Studio4m
Optional: Overview of IBM Watson Studio3m
Optional: Download data assets3m
Logistic Regression Demo (Activity)10m
Summary/Review4m
3 ejercicios de práctica
Logistic Regression4m
Logistic Regression Demo2m
End of Module10m
Semana
2

Semana 2

1 hora para completar

K Nearest Neighbors

1 hora para completar
7 videos (Total 50 minutos), 2 lecturas, 3 cuestionarios
7 videos
K Nearest Neighbors Decision Boundary3m
K Nearest Neighbors Distance Measurement8m
K Nearest Neighbors with Feature Scaling5m
K Nearest Neighbors Notebook - Part 19m
K Nearest Neighbors Notebook - Part 26m
K Nearest Neighbors Notebook - Part 311m
2 lecturas
K Nearest Neighbors Demo (Activity)3m
Summary/Review1m
3 ejercicios de práctica
K Nearest Neighbors3m
N Nearest Neighbors Demo5m
End of Module15m
2 horas para completar

Support Vector Machines

2 horas para completar
11 videos (Total 67 minutos), 2 lecturas, 4 cuestionarios
11 videos
Classification with Support Vector Machines2m
The Support Vector Machines Cost Function5m
Regularization in Support Vector Machines6m
Introduction to Support Vector Machines Gaussian Kernels2m
Support Vector Machines Gaussian Kernels - Part 14m
Support Vector Machines Gaussian Kernels - Part 24m
Implementing Support Vector Machines Kernel Models8m
Support Vector Machines Notebook - Part 18m
Support Vector Machines Notebook - Part 28m
Support Vector Machines Notebook - Part 310m
2 lecturas
Support Vector Machines Demo (Activity)3m
Summary/Review2m
4 ejercicios de práctica
Support Vector Machines5m
Support Vector Machines Kernels3m
Support Vector Machines Demo3m
End of Module10m
Semana
3

Semana 3

2 horas para completar

Decision Trees

2 horas para completar
8 videos (Total 60 minutos), 2 lecturas, 3 cuestionarios
8 videos
Building a Decision Tree6m
Entropy-based Splitting2m
Other Decision Tree Splitting Criteria4m
Pros and Cons of Decision Trees5m
Decision Trees Notebook - Part 16m
Decision Trees Notebook - Part 28m
Decision Trees Notebook - Part 315m
2 lecturas
Decision Trees Demo (Activity)10m
Summary/Review3m
3 ejercicios de práctica
Decision Trees4m
Decision Trees Demo3m
End of Module10m
2 horas para completar

Ensemble Models

2 horas para completar
15 videos (Total 93 minutos), 3 lecturas, 6 cuestionarios
15 videos
Ensemble Based Methods and Bagging - Part 21m
Ensemble Based Methods and Bagging - Part 33m
Random Forest7m
Bagging Notebook - Part 16m
Bagging Notebook - Part 26m
Bagging Notebook - Part 39m
Review of Bagging4m
Overview of Boosting3m
Adaboost and Gradient Boosting Overview7m
Adaboost and Gradient Boosting Syntax4m
Stacking7m
Boosting Notebook - Part 17m
Boosting Notebook - Part 215m
Boosting Notebook - Part 35m
3 lecturas
Bagging Demo (Activity)3m
Boosting and Stacking Demo (Activity)3m
Summary/Review10m
6 ejercicios de práctica
Bagging5m
Random Forest3m
Bagging Demo3m
Boosting and Stacking5m
Boosting and Stacking Demo5m
End of Module10m
Semana
4

Semana 4

2 horas para completar

Modeling Unbalanced Classes

2 horas para completar
6 videos (Total 30 minutos), 1 lectura, 3 cuestionarios
6 videos
Upsampling and Downsampling6m
Modeling Approaches: Weighting and Stratified Sampling3m
Modeling Approaches: Random and Synthetic Oversampling5m
Modeling Approaches: Nearing Neighbor Methods4m
Modeling Approaches: Blagging5m
1 lectura
Summary/Review10m
2 ejercicios de práctica
Modeling Unbalanced Classes4m
End of Module10m

Reseñas

Principales reseñas sobre SUPERVISED LEARNING: CLASSIFICATION

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Preguntas Frecuentes

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