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.

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 27 horas para completar

Sugerido: 9 hours/week...

Inglês

Legendas: Inglês

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

Aprox. 27 horas para completar

Sugerido: 9 hours/week...

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

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

1 vídeos

Welcome to the Course!1min

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®

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.

15 vídeos (Total 40 mín.), 16 leituras, 10 testes

15 videos

Introduction16s

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

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.

14 vídeos (Total 47 mín.), 11 leituras, 16 testes

14 videos

Introduction to Data Preparation and Algorithm Selection48s

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

In this module, you learn to build decision tree models as well as models based on ensembles, or combinations, of decision trees.

23 vídeos (Total 68 mín.), 12 leituras, 21 testes

23 videos

Introduction to Decision Trees and Ensembles of Trees41s

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

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

18 vídeos (Total 37 mín.), 10 leituras, 13 testes

18 videos

Introduction to Neural Networks29s

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

Semana

5

4 horas para concluir

Support Vector Machines

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

17 vídeos (Total 26 mín.), 5 leituras, 11 testes

17 videos

Introduction to Support Vector Machines46s

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 Machines23s

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 Model31s

Feature Space Approach for Nonlinearly Separable Data1min

Kernel Trick1min

Demo: Increasing the Flexibility of the Support Vector Machine2min

Model Interpretability1min

Demo: Adding Model Interpretability4min

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 5.012min

Question 5.022min

Question 5.032min

Question 5.042min

Support Vector Machines Quiz30min

Semana

6

4 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.

14 vídeos (Total 27 mín.), 2 leituras, 11 testes

14 videos

Introduction to Model Deployment36s

Essential Deployment Tasks41s

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: Exploring the Settings for Model Comparison and Selection2min

Scoring and Managing the Champion Model1min

Demo: Exploring the Features for Scoring and Managing a Model in Model Manager2min

Monitoring and Updating the Model1min

2 leituras

Numeric Measures of Model Performance by Prediction Type10min

Model Selection Statistics by Target Type10min

6 exercícios práticos

Question 6.012min

Question 6.022min

Question 6.032min

Question 6.042min

Question 6.052min

Model Deployment Quiz30min

Instrutores

Jeff Thompson

Senior Analytical Training Consultant

Education

Catherine Truxillo

Director, Analytical Education

Education

Perguntas Frequentes – FAQ

Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
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.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
What browser is recommended for accessing software in this course?
We recommend Chrome, Firefox or Safari when accessing the software.

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