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Sas Viya를 사용하는 머신 러닝

SAS

이 강좌에 대하여

최근 조회 48,945회

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.

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완료 시 수료증 획득

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 47시간 필요

영어

자막: 영어

강사

강사 평가

4.44/5 (11개의 평가)

Jeff Thompson

Senior Analytical Training Consultant

Education

4,984명의 학습자

1 Course

Catherine Truxillo

Director, Analytical Education

Education

4,984명의 학습자

1 Course

공유 가능한 수료증

완료 시 수료증 획득

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 47시간 필요

영어

자막: 영어

제공자:

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.

강의 계획 - 이 강좌에서 배울 내용

주

주 1

완료하는 데 1시간 필요

Course Overview

In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.

완료하는 데 1시간 필요

1개 동영상 (총 1분), 3 개의 읽기 자료, 1 개의 테스트

1개의 동영상

Welcome to the Course!1m

3개의 읽기 자료

Learner Prerequisites

Using SAS® Viya® for Learners with This Course (Required)10m

Using Forums and Getting Help10m

완료하는 데 8시간 필요

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.

완료하는 데 8시간 필요

15개 동영상 (총 40분), 16 개의 읽기 자료, 10 개의 테스트

15개의 동영상

Introduction16

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 Preparation55

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

16개의 읽기 자료

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

7개 연습문제

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

주

주 2

완료하는 데 9시간 필요

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.

완료하는 데 9시간 필요

14개 동영상 (총 47분), 11 개의 읽기 자료, 16 개의 테스트

14개의 동영상

Introduction to Data Preparation and Algorithm Selection48

Exploring the Data1m

Demo: Exploring the Data4m

Replacing Incorrect Values1m

Demo: Replacing Incorrect Values Starting on the Data Tab7m

Feature Creation27

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

11개의 읽기 자료

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

9개 연습문제

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

주

주 3

완료하는 데 11시간 필요

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.

완료하는 데 11시간 필요

23개 동영상 (총 68분), 12 개의 읽기 자료, 21 개의 테스트

23개의 동영상

Introduction to Decision Trees and Ensembles of Trees41

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 Model25

Demo: Modifying the Structure Parameters1m

Recursive Partitioning3m

Splitting Criteria4m

Split Search9m

Demo: Modifying the Recursive Partitioning Parameters1m

Optimizing the Complexity of a Decision Tree Model39

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

12개의 읽기 자료

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

11개 연습문제

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

주

주 4

완료하는 데 8시간 필요

Neural Networks

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

완료하는 데 8시간 필요

18개 동영상 (총 37분), 10 개의 읽기 자료, 13 개의 테스트

18개의 동영상

Introduction to Neural Networks29

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 Networks24

Demo: Building a Neural Network Using the Default Settings3m

Improving the Neural Network Model22

Neural Network Architectures4m

Activation Functions1m

Shaping the Sigmoid2m

Demo: Modifying the Neural Network Architecture1m

Optimizing the Complexity of a Neural Network Model40

Weight Decay1m

Early Stopping2m

Regularizing and Tuning the Hyperparameters of a Neural Network Model32

Demo: Modifying the Learning and Optimization Parameters2m

10개의 읽기 자료

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

8개 연습문제

Question 4.0130m

Question 4.0230m

Question 4.0330m

Question 4.0430m

Question 4.0530m

Question 4.0630m

Question 4.0730m

Neural Networks Quiz30m

주

주 5

완료하는 데 6시간 필요

Support Vector Machines

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

완료하는 데 6시간 필요

17개 동영상 (총 26분), 5 개의 읽기 자료, 11 개의 테스트

17개의 동영상

Introduction to Support Vector Machines46

Support Vector Machines as Classifier Models2m

Mathematical Definition of a Support Vector Machine1m

Maximum-Margin Hyperplane and Support Vectors1m

Essential Discovery Tasks for Support Vector Machines23

Demo: Building a Support Vector Machine Using the Default Settings2m

Improving the Support Vector Machine Model1m

Optimization Problem1m

Accounting for Errors with Nonlinearly Separable Data51

Demo: Modifying the Methods of Solution Parameters1m

Optimizing the Complexity of the Support Vector Machine Model31

Feature Space Approach for Nonlinearly Separable Data1m

Kernel Trick1m

Demo: Increasing the Flexibility of the Support Vector Machine2m

Model Interpretability1m

Demo: Adding Model Interpretability4m

Regularizing and Tuning the Hyperparameters of the Support Vector Machine Model22

5개의 읽기 자료

Dot Products10m

Constraints for Optimization10m

Lagrange Approach for Estimation10m

Model Interpretability Plots10m

Autotuning Options for Support Vector Machines in Model Studio10m

5개 연습문제

Question 5.0130m

Question 5.0230m

Question 5.0330m

Question 5.0430m

Support Vector Machines Quiz30m

주

주 6

완료하는 데 6시간 필요

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.

완료하는 데 6시간 필요

14개 동영상 (총 27분), 2 개의 읽기 자료, 11 개의 테스트

14개의 동영상

Introduction to Model Deployment36

Essential Deployment Tasks41

Selecting a Model45

Numeric Measures of Model Performance1m

Confusion Matrix for Decision Predictions1m

ROC Charts and the C-Statistic3m

Charts Based on Response Rate: CPH and Lift3m

Ways of Comparing Models in Model Studio1m

Demo: Comparing Models within a Pipeline2m

Demo: Comparing Models across Pipelines2m

Demo: Exploring the Settings for Model Comparison and Selection2m

Scoring and Managing the Champion Model1m

Demo: Exploring the Features for Scoring and Managing a Model in Model Manager2m

Monitoring and Updating the Model1m

2개의 읽기 자료

Numeric Measures of Model Performance by Prediction Type10m

Model Selection Statistics by Target Type10m

6개 연습문제

Question 6.0130m

Question 6.0230m

Question 6.0330m

Question 6.0430m

Question 6.0530m

Model Deployment Quiz30m

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Sas Viya를 사용하는 머신 러닝

Sas Viya를 사용하는 머신 러닝

이 강좌에 대하여

강사

Jeff Thompson

Catherine Truxillo

제공자:

SAS

강의 계획 - 이 강좌에서 배울 내용

주 1

Course Overview

Getting Started with Machine Learning using SAS® Viya®

주 2

Data Preparation and Algorithm Selection

주 3

Decision Trees and Ensembles of Trees

주 4

Neural Networks

주 5

Support Vector Machines

주 6

Model Deployment

검토

SAS VIYA를 사용하는 머신 러닝의 최상위 리뷰

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인기 온라인 전문 분야

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Sas Viya를 사용하는 머신 러닝

Sas Viya를 사용하는 머신 러닝

이 강좌에 대하여

강사

Jeff Thompson

Catherine Truxillo

제공자:

SAS

강의 계획 - 이 강좌에서 배울 내용

주 1

Course Overview

Getting Started with Machine Learning using SAS® Viya®

주 2

Data Preparation and Algorithm Selection

주 3

Decision Trees and Ensembles of Trees

주 4

Neural Networks

주 5

Support Vector Machines

주 6

Model Deployment

검토

SAS VIYA를 사용하는 머신 러닝의 최상위 리뷰

자주 묻는 질문

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