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.
シラバス - 本コースの学習内容
週
11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
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
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
26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
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
37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
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
44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
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
4.6
5件のレビューSas Viyaを使った機械学習 からの人気レビュー
Easy to follow even with limited statistics knowledge.\n\nInstructors teach the basics to get you started and best of all the software is included so you can practice.
講師
Jeff Thompson
Senior Analytical Training ConsultantEducation
Catherine Truxillo
Director, Analytical EducationEducation
SASについて
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.
よくある質問
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返金ポリシーについて教えてください。
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We recommend Chrome, Firefox or Safari when accessing the software.
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