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 avanzado
Aprox. 12 horas para completar
Inglés (English)

Habilidades que obtendrás

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming
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 avanzado
Aprox. 12 horas para completar
Inglés (English)

ofrecido por

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IBM

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

6 horas para completar

Data transforms and feature engineering

6 horas para completar
6 videos (Total 31 minutos), 14 lecturas, 5 cuestionarios
6 videos
Introduction to Class Imbalance1m
Class Imbalance Deep Dive9m
Introduction to Dimensionality Reduction2m
Dimension Reduction13m
Case Study Intro / Feature Engineering1m
14 lecturas
Data Transformation: Through the eyes of our Working Example3m
Transforms with scikit-learn3m
Pipelines3m
Class imbalance: Through the Eyes of our Working Example3m
Class Imbalance5m
Sampling Techniques2m
Models that Naturally Handle Imbalance2m
Data Bias2m
Dimensionality Reduction: Through the Eyes of Our Working Example3m
Why is Dimensionality Reduction Important?3m
Dimensionality Reduction and Topic models5m
Topic modeling: Through the Eyes of our Working Example3m
Getting Started with the Topic Modeling Case Study (hands-on)2h
Data Transforms and Feature Engineering: Summary/Review5m
5 ejercicios de práctica
Getting Started: Check for Understanding30m
Class Imbalance, Data Bias: Check for Understanding30m
Dimensionality Reduction: Check for Understanding3m
CASE STUDY - Topic Modeling: Check for Understanding30m
Data Transforms and Feature Engineering: End of Module Quiz10m
Semana
2

Semana 2

6 horas para completar

Pattern recognition and data mining best practices

6 horas para completar
5 videos (Total 16 minutos), 11 lecturas, 5 cuestionarios
5 videos
Introduction to Outliers2m
Outlier Detection3m
Introduction to Unsupervised learning2m
Unsupervised Learning5m
11 lecturas
ai360: Through the Eyes of our Working Example3m
Introduction to ai360 (hands-on)15m
Outlier Detection: Through the Eyes of our Working Example3m
Outliers3m
Unsupervised learning: Through the Eyes of our Working Example3m
An Overview of Unsupervised Learning2m
Clustering3m
Clustering Evaluation3m
Clustering: Through the Eyes of our Working Example3m
Getting Started with the Clustering Case Study (hands-on)2h 10m
Pattern Recognition and Data Mining Best Practices: Summary/Review4m
5 ejercicios de práctica
ai360 Tutorial: Check for Understanding30m
Outlier Detection: Check for Understanding30m
Unsupervised Learning: Check for Understanding30m
CASE STUDY - Clustering: Check for Understanding30m
Pattern Recognition and Data Mining Best Practices: End of Module Quiz12m

Reseñas

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Acerca de Programa especializado: IBM AI Enterprise Workflow

IBM AI Enterprise Workflow

Preguntas Frecuentes

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