This course covers the theoretical foundation for different techniques associated with supervised machine learning models. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. A series of demonstrations and exercises is used to reinforce the concepts and the analytical approach to solving business problems.
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Aprendizaje automático con Sas Viya
SASAcerca de este Curso
45,810 vistas recientes
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. 47 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
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. 47 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
SAS
Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.
Programa - Qué aprenderás en este curso
1 hora para completar
Course Overview
In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.
1 hora para completar
1 video (Total 1 minutos), 3 lecturas, 1 cuestionario
1 video
3 lecturas
Learner Prerequisites
Using SAS® Viya® for Learners with This Course (Required)10m
Using Forums and Getting Help10m
8 horas para completar
Getting Started with Machine Learning using SAS® Viya®
In this module, you learn how you can meet today's business challenges with machine learning using SAS® Viya®. You start working on the project that runs throughout the course.
8 horas para completar
15 videos (Total 40 minutos), 16 lecturas, 10 cuestionarios
15 videos
Introduction16s
Machine Learning in SAS Viya2m
Analytics Life Cycle1m
Case Study: Customer Churn2m
SAS Viya Tools for SAS Visual Data Mining and Machine Learning1m
Demo: Creating a Project4m
Predictive Modeling5m
Importance of Data Preparation55s
Essential Data Tasks1m
Dividing the Data3m
Addressing Rare Events Using Event-Based Sampling3m
Demo: Modifying the Data Partition4m
Managing Missing Values3m
Demo: Building a Pipeline from a Basic Template4m
SAS Viya in the SAS Platform: Architecture1m
16 lecturas
Applications of Prediction-Based Decision Making10m
Advantages of the SAS Platform10m
Case Study: Data Dictionary10m
SAS Drive and the Applications Menu10m
Importing Data from a Local Source10m
SAS Viya Tools for Data Preparation10m
Cross Validation for Small Data Sets10m
Global Metadata10m
Managing Missing Values: Details10m
Pipeline Templates in Model Studio10m
Logistic Regression10m
SAS Cloud Analytic Services10m
SAS Viya: A Shift in Mindset10m
Data Sources and CAS10m
Interfaces and Products10m
SAS Visual Data Mining and Machine Learning10m
7 ejercicios de práctica
Question 1.0130m
Question 1.0230m
Question 1.0330m
Question 1.0430m
Question 1.0530m
Question 1.0630m
Getting Started with Machine Learning and SAS Viya30m
9 horas para completar
Data Preparation and Algorithm Selection
In this module, you learn to explore the data and finish preparing the data for analysis. You also learn some general considerations for selecting an algorithm.
9 horas para completar
14 videos (Total 47 minutos), 11 lecturas, 16 cuestionarios
14 videos
Exploring the Data1m
Demo: Exploring the Data4m
Replacing Incorrect Values1m
Demo: Replacing Incorrect Values Starting on the Data Tab7m
Feature Creation27s
Text Mining1m
Demo: Adding Text Mining Features7m
Using Transformations to Handle Extreme or Unusual Values3m
Demo: Transforming Inputs5m
Selecting Useful Inputs4m
Demo: Selecting Features6m
Demo: Saving a Pipeline to the Exchange1m
Essential Discovery Tasks and Selecting an Algorithm1m
11 lecturas
Data Mining Preprocessing Nodes in Model Studio10m
Replacing Incorrect Values Starting with the Manage Variables Node10m
Singular Value Decomposition10m
Feature Extraction Node10m
Finding the Best Transformation in Model Studio10m
Feature Selection and the Variable Selection Node in Model Studio: Details10m
Variable Clustering10m
Best Practices for Common Data Preparation Challenges10m
Automated Feature Engineering Pipeline Template10m
Considerations for Selecting an Algorithm10m
Comparison of Modeling Algorithms10m
9 ejercicios de práctica
Question 2.0130m
Question 2.0230m
Question 2.0330m
Question 2.0430m
Question 2.0530m
Question 2.0630m
Question 2.0730m
Question 2.085m
Data Preparation and Algorithm Selection Quiz30m
11 horas para completar
Decision Trees and Ensembles of Trees
In this module, you learn to build decision tree models as well as models based on ensembles, or combinations, of decision trees.
11 horas para completar
23 videos (Total 68 minutos), 12 lecturas, 21 cuestionarios
23 videos
Basics of Decision Trees2m
Demo: Building a Decision Tree Model Using the Default Settings7m
Decision Trees for Categorical Targets: Classification Trees3m
Decision Trees for Interval Targets: Regression Trees2m
Improving the Decision Tree Model25s
Demo: Modifying the Structure Parameters1m
Recursive Partitioning3m
Splitting Criteria4m
Split Search9m
Demo: Modifying the Recursive Partitioning Parameters1m
Optimizing the Complexity of a Decision Tree Model39s
Pruning3m
Demo: Modifying the Pruning Parameters2m
Regularizing and Tuning the Hyperparameters of a Machine Learning Model2m
Building Ensemble Models1m
Perturb and Combine Methods5m
Bagging2m
Boosting1m
Comparison of Tree-Based Models1m
Demo: Building a Gradient Boosting Model3m
Forest Models3m
Demo: Building a Forest Model4m
12 lecturas
Impurity Reduction Measures for Categorical and Interval Targets10m
Splitting Criteria in Model Studio10m
Adjustments in a Split Search10m
Missing Values in Decision Trees in Model Studio10m
Surrogate Splits10m
Calculating Variable Importance for Surrogate Splits10m
Bottom-Up Pruning Requirements10m
Pruning Options in Model Studio10m
Autotuning Options for Decision Trees in Model Studio10m
Gradient Boosting Models10m
Autotuning Options for Gradient Boosting in Model Studio10m
Autotuning Options for Forests in Model Studio10m
11 ejercicios de práctica
Question 3.01
Question 3.0230m
Question 3.0330m
Question 3.0430m
Question 3.0530m
Think About It30m
Question 3.0630m
Question 3.0730m
Question 3.08
Question 3.0930m
Decision Trees and Ensembles of Trees Quiz30m
8 horas para completar
Neural Networks
In this module, you learn to build neural network models.
8 horas para completar
18 videos (Total 37 minutos), 10 lecturas, 13 cuestionarios
18 videos
Beyond Traditional Regression: Neural Networks3m
Limitations of Neural Networks2m
Basics of Neural Networks3m
Estimating Weights and Making Predictions3m
Learning Process2m
Essential Discovery Tasks for Neural Networks24s
Demo: Building a Neural Network Using the Default Settings3m
Improving the Neural Network Model22s
Neural Network Architectures4m
Activation Functions1m
Shaping the Sigmoid2m
Demo: Modifying the Neural Network Architecture1m
Optimizing the Complexity of a Neural Network Model40s
Weight Decay1m
Early Stopping2m
Regularizing and Tuning the Hyperparameters of a Neural Network Model32s
Demo: Modifying the Learning and Optimization Parameters2m
10 lecturas
Standardization Methods10m
Iterative Updating in Numerical Optimization10m
Numerical Optimization Methods in Model Studio10m
Deviance Measures in Model Studio10m
Calculating the Number of Parameters10m
Deep Learning10m
Hidden Layer Activation Functions in Model Studio10m
Target Layer Activation Functions and Error Functions in Model Studio10m
Selected Hyperparameters Related to the Learning Process in Model Studio10m
Autotuning Options for Neural Networks in Model Studio10m
8 ejercicios de práctica
Question 4.0130m
Question 4.0230m
Question 4.0330m
Question 4.0430m
Question 4.0530m
Question 4.0630m
Question 4.0730m
Neural Networks Quiz30m
Reseñas
Principales reseñas sobre APRENDIZAJE AUTOMÁTICO CON SAS VIYA
por TA5 de jul. de 2019
Easy to follow even with limited statistics knowledge. Instructors teach the basics to get you started and best of all the software is included so you can practice.
por RM7 de abr. de 2020
I found this course SAS Viya really excellent and useful for Data Scientist. I would recommend it to anyone interested in learning and deploy machine learning.
por KK11 de may. de 2020
nice course for those who already knew machine learning concepts and wants to learn SAS viya for creating models.
por PP11 de jun. de 2020
A well designed and thoughtful course explaining the key concepts of machine learning
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
- es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
- Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si compro el Certificado?
Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.
Is financial aid available?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el vínculo de Ayuda económica que está debajo del botón 'Inscribirse' a la izquierda. Se te pedirá que completes una solicitud y recibirás una notificación cuando se apruebe. Obtén más información.
What browser is recommended for accessing software in this course?
We recommend Chrome, Firefox or Safari when accessing the software.
¿Recibiré crédito universitario por completar el Curso?
Este Curso no otorga crédito universitario, pero algunas universidades pueden aceptar los Certificados del curso para obtener crédito. Consulta con tu institución para obtener más información. Los Títulos en línea y los Certificados Mastertrack™ de Coursera brindan la oportunidad de obtener créditos universitarios.
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