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Aprox. 21 horas para completar

Sugerido: 10 hours/week...

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Legendas: Inglês

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 21 horas para completar

Sugerido: 10 hours/week...

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
1 hora para concluir

Course Overview

1 vídeo (Total 1 mín.), 3 leituras, 1 teste
1 vídeos
3 leituras
Learner Prerequisites
Using SAS® Viya® for Learners with This Course (Required)10min
Using Forums and Getting Help10min
5 horas para concluir

Getting Started with Machine Learning using SAS® Viya®

15 vídeos (Total 40 mín.), 16 leituras, 10 testes
15 videos
Machine Learning in SAS Viya2min
Analytics Life Cycle1min
Case Study: Customer Churn2min
SAS Viya Tools for SAS Visual Data Mining and Machine Learning1min
Demo: Creating a Project4min
Predictive Modeling5min
Importance of Data Preparation55s
Essential Data Tasks1min
Dividing the Data3min
Addressing Rare Events Using Event-Based Sampling3min
Demo: Modifying the Data Partition4min
Managing Missing Values3min
Demo: Building a Pipeline from a Basic Template4min
SAS Viya in the SAS Platform: Architecture1min
16 leituras
Applications of Prediction-Based Decision Making10min
Advantages of the SAS Platform10min
Case Study: Data Dictionary10min
SAS Drive and the Applications Menu10min
Importing Data from a Local Source10min
SAS Viya Tools for Data Preparation10min
Cross Validation for Small Data Sets10min
Global Metadata10min
Managing Missing Values: Details10min
Pipeline Templates in Model Studio10min
Logistic Regression10min
SAS Cloud Analytic Services10min
SAS Viya: A Shift in Mindset10min
Data Sources and CAS10min
Interfaces and Products10min
SAS Visual Data Mining and Machine Learning10min
7 exercícios práticos
Question 1.012min
Question 1.022min
Question 1.032min
Question 1.042min
Question 1.052min
Question 1.062min
Getting Started with Machine Learning and SAS Viya30min
Semana
2
6 horas para concluir

Data Preparation and Algorithm Selection

14 vídeos (Total 47 mín.), 11 leituras, 16 testes
14 videos
Exploring the Data1min
Demo: Exploring the Data4min
Replacing Incorrect Values1min
Demo: Replacing Incorrect Values Starting on the Data Tab7min
Feature Creation27s
Text Mining1min
Demo: Adding Text Mining Features7min
Using Transformations to Handle Extreme or Unusual Values3min
Demo: Transforming Inputs5min
Selecting Useful Inputs4min
Demo: Selecting Features6min
Demo: Saving a Pipeline to the Exchange1min
Essential Discovery Tasks and Selecting an Algorithm1min
11 leituras
Data Mining Preprocessing Nodes in Model Studio10min
Replacing Incorrect Values Starting with the Manage Variables Node10min
Singular Value Decomposition10min
Feature Extraction Node10min
Finding the Best Transformation in Model Studio10min
Feature Selection and the Variable Selection Node in Model Studio: Details10min
Variable Clustering10min
Best Practices for Common Data Preparation Challenges10min
Automated Feature Engineering Pipeline Template10min
Considerations for Selecting an Algorithm10min
Comparison of Modeling Algorithms10min
9 exercícios práticos
Question 2.012min
Question 2.022min
Question 2.032min
Question 2.042min
Question 2.052min
Question 2.062min
Question 2.072min
Question 2.085min
Data Preparation and Algorithm Selection Quiz30min
Semana
3
7 horas para concluir

Decision Trees and Ensembles of Trees

23 vídeos (Total 68 mín.), 12 leituras, 21 testes
23 videos
Basics of Decision Trees2min
Demo: Building a Decision Tree Model Using the Default Settings7min
Decision Trees for Categorical Targets: Classification Trees3min
Decision Trees for Interval Targets: Regression Trees2min
Improving the Decision Tree Model25s
Demo: Modifying the Structure Parameters1min
Recursive Partitioning3min
Splitting Criteria4min
Split Search9min
Demo: Modifying the Recursive Partitioning Parameters1min
Optimizing the Complexity of a Decision Tree Model39s
Pruning3min
Demo: Modifying the Pruning Parameters2min
Regularizing and Tuning the Hyperparameters of a Machine Learning Model2min
Building Ensemble Models1min
Perturb and Combine Methods5min
Bagging2min
Boosting1min
Comparison of Tree-Based Models1min
Demo: Building a Gradient Boosting Model3min
Forest Models3min
Demo: Building a Forest Model4min
12 leituras
Impurity Reduction Measures for Categorical and Interval Targets10min
Splitting Criteria in Model Studio10min
Adjustments in a Split Search10min
Missing Values in Decision Trees in Model Studio10min
Surrogate Splits10min
Calculating Variable Importance for Surrogate Splits10min
Bottom-Up Pruning Requirements10min
Pruning Options in Model Studio10min
Autotuning Options for Decision Trees in Model Studio10min
Gradient Boosting Models10min
Autotuning Options for Gradient Boosting in Model Studio10min
Autotuning Options for Forests in Model Studio10min
11 exercícios práticos
Question 3.01
Question 3.022min
Question 3.032min
Question 3.042min
Question 3.052min
Think About It2min
Question 3.062min
Question 3.072min
Question 3.08
Question 3.092min
Decision Trees and Ensembles of Trees Quiz30min
Semana
4
4 horas para concluir

Neural Networks

18 vídeos (Total 37 mín.), 10 leituras, 13 testes
18 videos
Beyond Traditional Regression: Neural Networks3min
Limitations of Neural Networks2min
Basics of Neural Networks3min
Estimating Weights and Making Predictions3min
Learning Process2min
Essential Discovery Tasks for Neural Networks24s
Demo: Building a Neural Network Using the Default Settings3min
Improving the Neural Network Model22s
Neural Network Architectures4min
Activation Functions1min
Shaping the Sigmoid2min
Demo: Modifying the Neural Network Architecture1min
Optimizing the Complexity of a Neural Network Model40s
Weight Decay1min
Early Stopping2min
Regularizing and Tuning the Hyperparameters of a Neural Network Model32s
Demo: Modifying the Learning and Optimization Parameters2min
10 leituras
Standardization Methods10min
Iterative Updating in Numerical Optimization10min
Numerical Optimization Methods in Model Studio10min
Deviance Measures in Model Studio10min
Calculating the Number of Parameters10min
Deep Learning10min
Hidden Layer Activation Functions in Model Studio10min
Target Layer Activation Functions and Error Functions in Model Studio10min
Selected Hyperparameters Related to the Learning Process in Model Studio10min
Autotuning Options for Neural Networks in Model Studio10min
8 exercícios práticos
Question 4.012min
Question 4.022min
Question 4.032min
Question 4.042min
Question 4.052min
Question 4.062min
Question 4.072min
Neural Networks Quiz30min
4.6
5 avaliaçõesChevron Right

Principais avaliações do Aprendizagem automática usando o Sas Viya

por TAJul 6th 2019

Easy to follow even with limited statistics knowledge.\n\nInstructors teach the basics to get you started and best of all the software is included so you can practice.

Instrutores

Avatar

Jeff Thompson

Senior Analytical Training Consultant
Education
Avatar

Catherine Truxillo

Director, Analytical Education
Education

Sobre 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....

Perguntas Frequentes – FAQ

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