このコースについて

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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%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約27時間で修了

推奨：9 hours/week...

英語

字幕：英語

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約27時間で修了

推奨：9 hours/week...

英語

字幕：英語

シラバス - 本コースの学習内容

週

1

1時間で修了

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件のビデオ（合計1分）, 3 readings, 1 quiz

1件のビデオ

Welcome to the Course!1 分

3件の学習用教材

Learner Prerequisites

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

Using Forums and Getting Help10 分

5時間で修了

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件のビデオ（合計40分）, 16 readings, 10 quizzes

15件のビデオ

Introduction16

Machine Learning in SAS Viya2 分

Analytics Life Cycle1 分

Case Study: Customer Churn2 分

SAS Viya Tools for SAS Visual Data Mining and Machine Learning1 分

Demo: Creating a Project4 分

Predictive Modeling5 分

Importance of Data Preparation55

Essential Data Tasks1 分

Dividing the Data3 分

Addressing Rare Events Using Event-Based Sampling3 分

Demo: Modifying the Data Partition4 分

Managing Missing Values3 分

Demo: Building a Pipeline from a Basic Template4 分

SAS Viya in the SAS Platform: Architecture1 分

16件の学習用教材

Applications of Prediction-Based Decision Making10 分

Advantages of the SAS Platform10 分

Case Study: Data Dictionary10 分

SAS Drive and the Applications Menu10 分

Importing Data from a Local Source10 分

SAS Viya Tools for Data Preparation10 分

Cross Validation for Small Data Sets10 分

Global Metadata10 分

Managing Missing Values: Details10 分

Pipeline Templates in Model Studio10 分

Logistic Regression10 分

SAS Cloud Analytic Services10 分

SAS Viya: A Shift in Mindset10 分

Data Sources and CAS10 分

Interfaces and Products10 分

SAS Visual Data Mining and Machine Learning10 分

7の練習問題

Question 1.012 分

Question 1.022 分

Question 1.032 分

Question 1.042 分

Question 1.052 分

Question 1.062 分

Getting Started with Machine Learning and SAS Viya30 分

週

2

6時間で修了

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件のビデオ（合計47分）, 11 readings, 16 quizzes

14件のビデオ

Introduction to Data Preparation and Algorithm Selection48

Exploring the Data1 分

Demo: Exploring the Data4 分

Replacing Incorrect Values1 分

Demo: Replacing Incorrect Values Starting on the Data Tab7 分

Feature Creation27

Text Mining1 分

Demo: Adding Text Mining Features7 分

Using Transformations to Handle Extreme or Unusual Values3 分

Demo: Transforming Inputs5 分

Selecting Useful Inputs4 分

Demo: Selecting Features6 分

Demo: Saving a Pipeline to the Exchange1 分

Essential Discovery Tasks and Selecting an Algorithm1 分

11件の学習用教材

Data Mining Preprocessing Nodes in Model Studio10 分

Replacing Incorrect Values Starting with the Manage Variables Node10 分

Singular Value Decomposition10 分

Feature Extraction Node10 分

Finding the Best Transformation in Model Studio10 分

Feature Selection and the Variable Selection Node in Model Studio: Details10 分

Variable Clustering10 分

Best Practices for Common Data Preparation Challenges10 分

Automated Feature Engineering Pipeline Template10 分

Considerations for Selecting an Algorithm10 分

Comparison of Modeling Algorithms10 分

9の練習問題

Question 2.012 分

Question 2.022 分

Question 2.032 分

Question 2.042 分

Question 2.052 分

Question 2.062 分

Question 2.072 分

Question 2.085 分

Data Preparation and Algorithm Selection Quiz30 分

週

3

7時間で修了

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件のビデオ（合計68分）, 12 readings, 21 quizzes

23件のビデオ

Introduction to Decision Trees and Ensembles of Trees41

Basics of Decision Trees2 分

Demo: Building a Decision Tree Model Using the Default Settings7 分

Decision Trees for Categorical Targets: Classification Trees3 分

Decision Trees for Interval Targets: Regression Trees2 分

Improving the Decision Tree Model25

Demo: Modifying the Structure Parameters1 分

Recursive Partitioning3 分

Splitting Criteria4 分

Split Search9 分

Demo: Modifying the Recursive Partitioning Parameters1 分

Optimizing the Complexity of a Decision Tree Model39

Pruning3 分

Demo: Modifying the Pruning Parameters2 分

Regularizing and Tuning the Hyperparameters of a Machine Learning Model2 分

Building Ensemble Models1 分

Perturb and Combine Methods5 分

Bagging2 分

Boosting1 分

Comparison of Tree-Based Models1 分

Demo: Building a Gradient Boosting Model3 分

Forest Models3 分

Demo: Building a Forest Model4 分

12件の学習用教材

Impurity Reduction Measures for Categorical and Interval Targets10 分

Splitting Criteria in Model Studio10 分

Adjustments in a Split Search10 分

Missing Values in Decision Trees in Model Studio10 分

Surrogate Splits10 分

Calculating Variable Importance for Surrogate Splits10 分

Bottom-Up Pruning Requirements10 分

Pruning Options in Model Studio10 分

Autotuning Options for Decision Trees in Model Studio10 分

Gradient Boosting Models10 分

Autotuning Options for Gradient Boosting in Model Studio10 分

Autotuning Options for Forests in Model Studio10 分

11の練習問題

Question 3.01

Question 3.022 分

Question 3.032 分

Question 3.042 分

Question 3.052 分

Think About It2 分

Question 3.062 分

Question 3.072 分

Question 3.08

Question 3.092 分

Decision Trees and Ensembles of Trees Quiz30 分

週

4

4時間で修了

Neural Networks

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

18件のビデオ（合計37分）, 10 readings, 13 quizzes

18件のビデオ

Introduction to Neural Networks29

Beyond Traditional Regression: Neural Networks3 分

Limitations of Neural Networks2 分

Basics of Neural Networks3 分

Estimating Weights and Making Predictions3 分

Learning Process2 分

Essential Discovery Tasks for Neural Networks24

Demo: Building a Neural Network Using the Default Settings3 分

Improving the Neural Network Model22

Neural Network Architectures4 分

Activation Functions1 分

Shaping the Sigmoid2 分

Demo: Modifying the Neural Network Architecture1 分

Optimizing the Complexity of a Neural Network Model40

Weight Decay1 分

Early Stopping2 分

Regularizing and Tuning the Hyperparameters of a Neural Network Model32

Demo: Modifying the Learning and Optimization Parameters2 分

10件の学習用教材

Standardization Methods10 分

Iterative Updating in Numerical Optimization10 分

Numerical Optimization Methods in Model Studio10 分

Deviance Measures in Model Studio10 分

Calculating the Number of Parameters10 分

Deep Learning10 分

Hidden Layer Activation Functions in Model Studio10 分

Target Layer Activation Functions and Error Functions in Model Studio10 分

Selected Hyperparameters Related to the Learning Process in Model Studio10 分

Autotuning Options for Neural Networks in Model Studio10 分

8の練習問題

Question 4.012 分

Question 4.022 分

Question 4.032 分

Question 4.042 分

Question 4.052 分

Question 4.062 分

Question 4.072 分

Neural Networks Quiz30 分

週

5

4時間で修了

Support Vector Machines

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

17件のビデオ（合計26分）, 5 readings, 11 quizzes

17件のビデオ

Introduction to Support Vector Machines46

Support Vector Machines as Classifier Models2 分

Mathematical Definition of a Support Vector Machine1 分

Maximum-Margin Hyperplane and Support Vectors1 分

Essential Discovery Tasks for Support Vector Machines23

Demo: Building a Support Vector Machine Using the Default Settings2 分

Improving the Support Vector Machine Model1 分

Optimization Problem1 分

Accounting for Errors with Nonlinearly Separable Data51

Demo: Modifying the Methods of Solution Parameters1 分

Optimizing the Complexity of the Support Vector Machine Model31

Feature Space Approach for Nonlinearly Separable Data1 分

Kernel Trick1 分

Demo: Increasing the Flexibility of the Support Vector Machine2 分

Model Interpretability1 分

Demo: Adding Model Interpretability4 分

Regularizing and Tuning the Hyperparameters of the Support Vector Machine Model22

5件の学習用教材

Dot Products10 分

Constraints for Optimization10 分

Lagrange Approach for Estimation10 分

Model Interpretability Plots10 分

Autotuning Options for Support Vector Machines in Model Studio10 分

5の練習問題

Question 5.012 分

Question 5.022 分

Question 5.032 分

Question 5.042 分

Support Vector Machines Quiz30 分

週

6

4時間で修了

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件のビデオ（合計27分）, 2 readings, 11 quizzes

14件のビデオ

Introduction to Model Deployment36

Essential Deployment Tasks41

Selecting a Model45

Numeric Measures of Model Performance1 分

Confusion Matrix for Decision Predictions1 分

ROC Charts and the C-Statistic3 分

Charts Based on Response Rate: CPH and Lift3 分

Ways of Comparing Models in Model Studio1 分

Demo: Comparing Models within a Pipeline2 分

Demo: Comparing Models across Pipelines2 分

Demo: Exploring the Settings for Model Comparison and Selection2 分

Scoring and Managing the Champion Model1 分

Demo: Exploring the Features for Scoring and Managing a Model in Model Manager2 分

Monitoring and Updating the Model1 分

2件の学習用教材

Numeric Measures of Model Performance by Prediction Type10 分

Model Selection Statistics by Target Type10 分

6の練習問題

Question 6.012 分

Question 6.022 分

Question 6.032 分

Question 6.042 分

Question 6.052 分

Model Deployment Quiz30 分

講師

Jeff Thompson

Senior Analytical Training Consultant

Education

Catherine Truxillo

Director, Analytical Education

Education

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Sas Viyaを使った機械学習