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16 avaliações

Jeff Thompson +1 mais instrutores

6.492 já se inscreveram

oferecido por

Aprendizagem automática usando o Sas Viya

SAS

Informações sobre o curso

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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. 
This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data. The SAS applications used in this course make machine learning possible without programming or coding.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Certificados compartilháveis

Tenha o certificado após a conclusão

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Nível intermediário

Aprox. 34 horas para completar

Inglês

Legendas: Inglês

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Certificados compartilháveis

Tenha o certificado após a conclusão

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Nível intermediário

Aprox. 34 horas para completar

Inglês

Legendas: Inglês

Instrutores

Classificação do instrutor

4,61/5 (16 classificações)

Jeff Thompson

Senior Analytical Training Consultant

Education

6.492 aprendizes

1 Curso

Catherine Truxillo

Director, Analytical Education

Education

6.492 aprendizes

1 Curso

oferecido 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 - O que você aprenderá com este curso

Semana

Semana 1

1 hora para concluir

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 concluir

1 vídeo (Total 2 mín.), 3 leituras, 1 teste

5 horas para concluir

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.

5 horas para concluir

15 vídeos (Total 43 mín.), 16 leituras, 9 testes

15 videos

Introduction13s

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 Project and Selecting Data5min

Importing Data from a Local Source1min

Predictive Modeling5min

Data Preparation1min

Dividing the Data3min

Addressing Rare Events Using Event-Based Sampling3min

Demo: Modifying the Data Partition5min

Managing Missing Values3min

Demo: Building a Pipeline from a Basic Template5min

SAS Viya in the SAS Platform: Architecture1min

16 leituras

Applications of Prediction-Based Decision Making10min

Case Study: Data Dictionary10min

SAS Drive and the Application Bar10min

Importing Data from a Local Source10min

SAS Viya Tools for Data Preparation10min

Cross Validation for Small Data Sets10min

Selecting Variables on the Data Tab10min

Global Metadata10min

Managing Missing Values: Details10min

Imputation Methods in Model Studio10min

Pipeline Templates in Model Studio10min

Logistic Regression10min

SAS Cloud Analytic Services10min

SAS Viya: A Shift in Mindset10min

Data Sources and CAS10min

Interfaces and Products10min

6 exercícios práticos

Question 1.025min

Question 1.035min

Question 1.045min

Question 1.055min

Question 1.065min

Getting Started with Machine Learning and SAS Viya30min

Semana

Semana 2

6 horas para concluir

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.

6 horas para concluir

14 vídeos (Total 44 mín.), 12 leituras, 16 testes

14 videos

Introduction to Data Preparation and Algorithm Selection45s

Exploring the Data1min

Demo: Exploring the Data4min

Replacing Incorrect Values1min

Demo: Replacing Incorrect Values Starting on the Data Tab6min

Feature Creation24s

Text Mining1min

Demo: Adding Text Mining Features6min

Using Transformations to Handle Extreme or Unusual Values3min

Demo: Transforming Inputs5min

Selecting Useful Inputs4min

Demo: Selecting Features5min

Demo: Saving a Pipeline to the Exchange1min

Starting the Discovery Phase of the Analytics Life Cycle1min

12 leituras

Data Mining Preprocessing Nodes in Model Studio10min

Risk Modeling Add-on for SAS Visual Data Mining and Machine Learning10min

Replacing Incorrect Values Starting with the Manage Variables Node10min

Singular Value Decomposition and the Text Mining Node10min

Feature Extraction Node10min

Transformations in Model Studio10min

Feature Selection and the Variable Selection Node: Details10min

Variable Clustering10min

Best Practices for Common Data Preparation Challenges10min

Feature Engineering in SAS Visual Data Mining and Machine Learning10min

Considerations for Selecting an Algorithm10min

Comparison of Modeling Algorithms10min

8 exercícios práticos

Question 2.015min

Question 2.025min

Question 2.035min

Question 2.045min

Question 2.055min

Question 2.075min

Question 2.085min

Data Preparation and Algorithm Selection Quiz30min

Semana

Semana 3

7 horas para concluir

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.

7 horas para concluir

23 vídeos (Total 69 mín.), 14 leituras, 18 testes

23 videos

Introduction to Decision Trees and Ensembles of Trees38s

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 Parameters2min

Recursive Partitioning3min

Splitting Criteria4min

Split Search9min

Demo: Modifying the Recursive Partitioning Parameters1min

Optimizing the Complexity of a Decision Tree Model36s

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

14 leituras

Score Code in Model Studio10min

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

Hyperparameter Optimization Methods10min

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

10 exercícios práticos

Question 3.015min

Question 3.025min

Question 3.035min

Question 3.045min

Question 3.055min

Question 3.065min

Question 3.075min

Question 3.085min

Question 3.095min

Decision Trees and Ensembles of Trees Quiz30min

Semana

Semana 4

4 horas para concluir

Neural Networks

In this module, you learn to build neural network models.

4 horas para concluir

19 vídeos (Total 42 mín.), 9 leituras, 10 testes

19 videos

Introduction to Neural Networks27s

Beyond Traditional Regression: Neural Networks4min

Overcoming the Limitations of Neural Networks3min

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 Settings2min

Improving the Neural Network Model18s

Neural Network Architectures4min

Activation Functions1min

Shaping the Sigmoid2min

Demo: Modifying the Neural Network Architecture1min

Optimizing the Complexity of a Neural Network Model37s

Weight Decay2min

Early Stopping2min

Regularizing and Tuning the Hyperparameters of a Neural Network Model32s

Network Learning Hyperparameters3min

Demo: Modifying the Learning and Optimization Parameters2min

9 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

Autotuning Options for Neural Networks in Model Studio10min

6 exercícios práticos

Question 4.015min

Question 4.02 - 4.0310min

Question 4.045min

Question 4.055min

Question 4.06 - 4.0710min

Neural Networks Quiz30min

Semana

Semana 5

4 horas para concluir

Support Vector Machines

In this module, you learn to build support vector machine models.

4 horas para concluir

17 vídeos (Total 31 mín.), 5 leituras, 10 testes

17 videos

Introduction to Support Vector Machines43s

Support Vector Machines as Classifier Models2min

Mathematical Definition of a Support Vector Machine1min

Maximum-Margin Hyperplane and Support Vectors1min

Essential Discovery Tasks for Support Vector Machines9s

Demo: Building a Support Vector Machine Using the Default Settings2min

Improving the Support Vector Machine Model1min

Optimization Problem1min

Accounting for Errors with Nonlinearly Separable Data51s

Demo: Modifying the Methods of Solution Parameters1min

Optimizing the Complexity of the Support Vector Machine Model28s

Feature Space Approach for Nonlinearly Separable Data1min

Kernel Trick1min

Demo: Increasing the Flexibility of the Support Vector Machine2min

Model Interpretability1min

Demo: Adding Model Interpretability10min

Regularizing and Tuning the Hyperparameters of the Support Vector Machine Model22s

5 leituras

Dot Products10min

Constraints for Optimization10min

Lagrange Approach for Estimation10min

Model Interpretability Plots10min

Autotuning Options for Support Vector Machines in Model Studio10min

5 exercícios práticos

Question 5.015min

Question 5.025min

Question 5.035min

Question 5.045min

Support Vector Machines Quiz30min

Semana

Semana 6

5 horas para concluir

Model Deployment

In this module, you learn how to select the model that best meets the requirements of your business challenge and put the model into production. You also learn about managing the model over time.

5 horas para concluir

16 vídeos (Total 36 mín.), 5 leituras, 13 testes

16 videos

Introduction to Model Deployment24s

Essential Deployment Tasks34s

Selecting a Model45s

Numeric Measures of Model Performance1min

Confusion Matrix for Decision Predictions1min

ROC Charts and the C-Statistic3min

Charts Based on Response Rate: CPH and Lift3min

Ways of Comparing Models in Model Studio1min

Demo: Comparing Models within a Pipeline2min

Demo: Comparing Models across Pipelines2min

Demo: Reviewing a Project Summary Report on the Insights Tab3min

Demo: Registering the Champion Model59s

Demo: Exploring the Settings for Model Selection2min

Scoring and Managing the Champion Model1min

Demo: Viewing the Score Code and Running a Scoring Test6min

Monitoring and Updating the Model1min

5 leituras

Numeric Measures of Model Performance by Prediction Type10min

Model Selection Statistics by Target Type10min

Score Code and Model Deployment10min

Introducing SAS Model Manager10min

SAS Model Manager Tabs10min

6 exercícios práticos

Question 6.015min

Question 6.025min

Question 6.035min

Question 6.045min

Question 6.055min

Model Deployment Quiz30min

Semana

Semana 7

2 horas para concluir

Additional Resources and Practice Exam

2 horas para concluir

1 vídeo (Total 6 mín.), 4 leituras, 1 teste

Avaliações

4.7

16 avaliações

5 stars
75,29%
4 stars
20%
3 stars
4,70%

Principais avaliações do APRENDIZAGEM AUTOMÁTICA USANDO O SAS VIYA

por EO5 de Jan de 2021

The content is great, although the platform failed after 5 days of inactivity. My account was deactivated and I was never able to recover it so I couldn't follow the tutorials.

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 Mai de 2020

nice course for those who already knew machine learning concepts and wants to learn SAS viya for creating models.

Visualizar todas as avaliações

Perguntas Frequentes – FAQ

Quando terei acesso às palestras e às tarefas?
O acesso a palestras e tarefas depende do tipo de inscrição. Se você participar de um curso como ouvinte, você poderá ver quase todo o conteúdo do curso gratuitamente. Para acessar tarefas valendo nota e obter um Certificado, você precisará adquirir a experiência do Certificado, durante ou após a participação como ouvinte. Se você não vir a opção de participar como ouvinte:
o curso pode não oferecer essa opção. Você pode experimentar um teste gratuito ou solicitar o auxílio financeiro.
Em vez disso, o curso pode oferecer 'Curso completo, sem Certificado'. Com esta opção, é possível ver todo o conteúdo do curso, enviar as avaliações necessárias e obter uma nota final. Isso também significa que você não poderá comprar uma experiência de Certificado.
O que recebo ao adquirir o Certificado?
Quando você adquire o Certificado, ganha acesso a todo o material do curso, incluindo avaliações com nota atribuída. Após concluir o curso, seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Existe algum auxílio financeiro disponível?
Sim, a Coursera oferece auxílio financeiro aos alunos que não podem pagar a taxa. Faça a solicitação clicando no link Auxílio financeiro, abaixo do botão "Inscreva-se" à esquerda. Você será solicitado a preencher um formulário e será notificado se for aprovado. Saiba mais.
What browser is recommended for accessing software in this course?
We recommend Chrome, Firefox or Safari when accessing the software.

Mais dúvidas? Visite o Central de Ajuda ao estudante.

Aprendizagem automática usando o Sas Viya

Aprendizagem automática usando o Sas Viya

Informações sobre o curso

Instrutores

Jeff Thompson

Catherine Truxillo

oferecido por

SAS

Programa - O que você aprenderá com este curso

Semana 1

Course Overview

Getting Started with Machine Learning using SAS® Viya®

Semana 2

Data Preparation and Algorithm Selection

Semana 3

Decision Trees and Ensembles of Trees

Semana 4

Neural Networks

Semana 5

Support Vector Machines

Semana 6

Model Deployment

Semana 7

Additional Resources and Practice Exam

Avaliações

Principais avaliações do APRENDIZAGEM AUTOMÁTICA USANDO O SAS VIYA

Perguntas Frequentes – FAQ

Comece ou impulsione sua carreira

Procurar temas populares

Popular courses and articles

Consiga um diploma ou certificado on-line

Coursera

Comunidade

Mais

Aprendizagem automática usando o Sas Viya

Aprendizagem automática usando o Sas Viya

Informações sobre o curso

Instrutores

Jeff Thompson

Catherine Truxillo

oferecido por

SAS

Programa - O que você aprenderá com este curso

Semana 1

Course Overview

Getting Started with Machine Learning using SAS® Viya®

Semana 2

Data Preparation and Algorithm Selection

Semana 3

Decision Trees and Ensembles of Trees

Semana 4

Neural Networks

Semana 5

Support Vector Machines

Semana 6

Model Deployment

Semana 7

Additional Resources and Practice Exam

Avaliações

Principais avaliações do APRENDIZAGEM AUTOMÁTICA USANDO O SAS VIYA

Perguntas Frequentes – FAQ

Coursera Footer

Comece ou impulsione sua carreira

Procurar temas populares

Popular courses and articles

Consiga um diploma ou certificado on-line

Coursera

Comunidade

Mais