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.
Machine Learning Using SAS Viya
oferecido por
SAS
Programa - O que você aprenderá com este curso
Semana
11 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
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
26 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
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
37 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
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
44 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
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
Instrutores
Jeff Thompson
Senior Analytical Training ConsultantEducation
Catherine Truxillo
Director, Analytical EducationEducation
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
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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