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Jeff Thompson +1명의 강사

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Sas Viya를 사용하는 머신 러닝

SAS

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최근 조회 7,051회

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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100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

중급 단계

완료하는 데 약 34시간 필요

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자막: 영어

유연한 마감일

일정에 따라 마감일을 재설정합니다.

공유 가능한 수료증

완료 시 수료증 획득

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

중급 단계

완료하는 데 약 34시간 필요

영어

자막: 영어

강사

강사 평가

4.61/5 (16개의 평가)

Jeff Thompson

Senior Analytical Training Consultant

Education

6,378명의 학습자

1개의 강좌

Catherine Truxillo

Director, Analytical Education

Education

6,378명의 학습자

1개의 강좌

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 계획표 - 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 2분), 3 개의 읽기 자료, 1 개의 테스트

완료하는 데 5시간 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 5시간 필요

15개 동영상 (총 43분), 16 개의 읽기 자료, 9 개의 테스트

15개의 동영상

Introduction13

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 Project and Selecting Data5m

Importing Data from a Local Source1m

Predictive Modeling5m

Data Preparation1m

Dividing the Data3m

Addressing Rare Events Using Event-Based Sampling3m

Demo: Modifying the Data Partition5m

Managing Missing Values3m

Demo: Building a Pipeline from a Basic Template5m

SAS Viya in the SAS Platform: Architecture1m

16개의 읽기 자료

Applications of Prediction-Based Decision Making10m

Case Study: Data Dictionary10m

SAS Drive and the Application Bar10m

Importing Data from a Local Source10m

SAS Viya Tools for Data Preparation10m

Cross Validation for Small Data Sets10m

Selecting Variables on the Data Tab10m

Global Metadata10m

Managing Missing Values: Details10m

Imputation Methods in Model Studio10m

Pipeline Templates in Model Studio10m

Logistic Regression10m

SAS Cloud Analytic Services10m

SAS Viya: A Shift in Mindset10m

Data Sources and CAS10m

Interfaces and Products10m

6개 연습문제

Question 1.025m

Question 1.035m

Question 1.045m

Question 1.055m

Question 1.065m

Getting Started with Machine Learning and SAS Viya30m

주

주 2

완료하는 데 6시간 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 6시간 필요

14개 동영상 (총 44분), 12 개의 읽기 자료, 16 개의 테스트

14개의 동영상

Introduction to Data Preparation and Algorithm Selection45

Exploring the Data1m

Demo: Exploring the Data4m

Replacing Incorrect Values1m

Demo: Replacing Incorrect Values Starting on the Data Tab6m

Feature Creation24

Text Mining1m

Demo: Adding Text Mining Features6m

Using Transformations to Handle Extreme or Unusual Values3m

Demo: Transforming Inputs5m

Selecting Useful Inputs4m

Demo: Selecting Features5m

Demo: Saving a Pipeline to the Exchange1m

Starting the Discovery Phase of the Analytics Life Cycle1m

12개의 읽기 자료

Data Mining Preprocessing Nodes in Model Studio10m

Risk Modeling Add-on for SAS Visual Data Mining and Machine Learning10m

Replacing Incorrect Values Starting with the Manage Variables Node10m

Singular Value Decomposition and the Text Mining Node10m

Feature Extraction Node10m

Transformations in Model Studio10m

Feature Selection and the Variable Selection Node: Details10m

Variable Clustering10m

Best Practices for Common Data Preparation Challenges10m

Feature Engineering in SAS Visual Data Mining and Machine Learning10m

Considerations for Selecting an Algorithm10m

Comparison of Modeling Algorithms10m

8개 연습문제

Question 2.015m

Question 2.025m

Question 2.035m

Question 2.045m

Question 2.055m

Question 2.075m

Question 2.085m

Data Preparation and Algorithm Selection Quiz30m

주

주 3

완료하는 데 7시간 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 7시간 필요

23개 동영상 (총 69분), 14 개의 읽기 자료, 18 개의 테스트

23개의 동영상

Introduction to Decision Trees and Ensembles of Trees38

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 Parameters2m

Recursive Partitioning3m

Splitting Criteria4m

Split Search9m

Demo: Modifying the Recursive Partitioning Parameters1m

Optimizing the Complexity of a Decision Tree Model36

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

14개의 읽기 자료

Score Code in Model Studio10m

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

Hyperparameter Optimization Methods10m

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

10개 연습문제

Question 3.015m

Question 3.025m

Question 3.035m

Question 3.045m

Question 3.055m

Question 3.065m

Question 3.075m

Question 3.085m

Question 3.095m

Decision Trees and Ensembles of Trees Quiz30m

주

주 4

완료하는 데 4시간 필요

Neural Networks

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

완료하는 데 4시간 필요

19개 동영상 (총 42분), 9 개의 읽기 자료, 10 개의 테스트

19개의 동영상

Introduction to Neural Networks27

Beyond Traditional Regression: Neural Networks4m

Overcoming the Limitations of Neural Networks3m

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 Settings2m

Improving the Neural Network Model18

Neural Network Architectures4m

Activation Functions1m

Shaping the Sigmoid2m

Demo: Modifying the Neural Network Architecture1m

Optimizing the Complexity of a Neural Network Model37

Weight Decay2m

Early Stopping2m

Regularizing and Tuning the Hyperparameters of a Neural Network Model32

Network Learning Hyperparameters3m

Demo: Modifying the Learning and Optimization Parameters2m

9개의 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

Autotuning Options for Neural Networks in Model Studio10m

6개 연습문제

Question 4.015m

Question 4.02 - 4.0310m

Question 4.045m

Question 4.055m

Question 4.06 - 4.0710m

Neural Networks Quiz30m

주

주 5

완료하는 데 4시간 필요

Support Vector Machines

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

완료하는 데 4시간 필요

17개 동영상 (총 31분), 5 개의 읽기 자료, 10 개의 테스트

17개의 동영상

Introduction to Support Vector Machines43

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 Machines9

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 Model28

Feature Space Approach for Nonlinearly Separable Data1m

Kernel Trick1m

Demo: Increasing the Flexibility of the Support Vector Machine2m

Model Interpretability1m

Demo: Adding Model Interpretability10m

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 5.015m

Question 5.025m

Question 5.035m

Question 5.045m

Support Vector Machines Quiz30m

주

주 6

완료하는 데 5시간 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 5시간 필요

16개 동영상 (총 36분), 5 개의 읽기 자료, 13 개의 테스트

16개의 동영상

Introduction to Model Deployment24

Essential Deployment Tasks34

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: Reviewing a Project Summary Report on the Insights Tab3m

Demo: Registering the Champion Model59

Demo: Exploring the Settings for Model Selection2m

Scoring and Managing the Champion Model1m

Demo: Viewing the Score Code and Running a Scoring Test6m

Monitoring and Updating the Model1m

5개의 읽기 자료

Numeric Measures of Model Performance by Prediction Type10m

Model Selection Statistics by Target Type10m

Score Code and Model Deployment10m

Introducing SAS Model Manager10m

SAS Model Manager Tabs10m

6개 연습문제

Question 6.015m

Question 6.025m

Question 6.035m

Question 6.045m

Question 6.055m

Model Deployment Quiz30m

주

주 7

완료하는 데 2시간 필요

Additional Resources and Practice Exam

완료하는 데 2시간 필요

1개 동영상 (총 6분), 4 개의 읽기 자료, 1 개의 테스트

검토

4.7

16개의 리뷰

5 stars
75.29%
4 stars
20%
3 stars
4.70%

SAS VIYA를 사용하는 머신 러닝의 최상위 리뷰

EO 제공2021년 1월 5일

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.

TA 제공2019년 7월 5일

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.

RM 제공2020년 4월 7일

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.

KK 제공2020년 5월 11일

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

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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 계획표 - 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

주 7

Additional Resources and Practice Exam

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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 계획표 - 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

주 7

Additional Resources and Practice Exam

검토

SAS VIYA를 사용하는 머신 러닝의 최상위 리뷰

자주 묻는 질문

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