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中级

英语(English)

字幕:英语(English)

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中级

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 1 小时

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 个视频 (总计 1 分钟), 3 个阅读材料, 1 个测验
1 个视频
3 个阅读材料
Learner Prerequisites
Using SAS® Viya® for Learners with This Course (Required)10分钟
Using Forums and Getting Help10分钟
完成时间为 5 小时

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.

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15 个视频 (总计 40 分钟), 16 个阅读材料, 10 个测验
15 个视频
Machine Learning in SAS Viya2分钟
Analytics Life Cycle1分钟
Case Study: Customer Churn2分钟
SAS Viya Tools for SAS Visual Data Mining and Machine Learning1分钟
Demo: Creating a Project4分钟
Predictive Modeling5分钟
Importance of Data Preparation55
Essential Data Tasks1分钟
Dividing the Data3分钟
Addressing Rare Events Using Event-Based Sampling3分钟
Demo: Modifying the Data Partition4分钟
Managing Missing Values3分钟
Demo: Building a Pipeline from a Basic Template4分钟
SAS Viya in the SAS Platform: Architecture1分钟
16 个阅读材料
Applications of Prediction-Based Decision Making10分钟
Advantages of the SAS Platform10分钟
Case Study: Data Dictionary10分钟
SAS Drive and the Applications Menu10分钟
Importing Data from a Local Source10分钟
SAS Viya Tools for Data Preparation10分钟
Cross Validation for Small Data Sets10分钟
Global Metadata10分钟
Managing Missing Values: Details10分钟
Pipeline Templates in Model Studio10分钟
Logistic Regression10分钟
SAS Cloud Analytic Services10分钟
SAS Viya: A Shift in Mindset10分钟
Data Sources and CAS10分钟
Interfaces and Products10分钟
SAS Visual Data Mining and Machine Learning10分钟
7 个练习
Question 1.012分钟
Question 1.022分钟
Question 1.032分钟
Question 1.042分钟
Question 1.052分钟
Question 1.062分钟
Getting Started with Machine Learning and SAS Viya30分钟
2
完成时间为 6 小时

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.

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14 个视频 (总计 47 分钟), 11 个阅读材料, 16 个测验
14 个视频
Exploring the Data1分钟
Demo: Exploring the Data4分钟
Replacing Incorrect Values1分钟
Demo: Replacing Incorrect Values Starting on the Data Tab7分钟
Feature Creation27
Text Mining1分钟
Demo: Adding Text Mining Features7分钟
Using Transformations to Handle Extreme or Unusual Values3分钟
Demo: Transforming Inputs5分钟
Selecting Useful Inputs4分钟
Demo: Selecting Features6分钟
Demo: Saving a Pipeline to the Exchange1分钟
Essential Discovery Tasks and Selecting an Algorithm1分钟
11 个阅读材料
Data Mining Preprocessing Nodes in Model Studio10分钟
Replacing Incorrect Values Starting with the Manage Variables Node10分钟
Singular Value Decomposition10分钟
Feature Extraction Node10分钟
Finding the Best Transformation in Model Studio10分钟
Feature Selection and the Variable Selection Node in Model Studio: Details10分钟
Variable Clustering10分钟
Best Practices for Common Data Preparation Challenges10分钟
Automated Feature Engineering Pipeline Template10分钟
Considerations for Selecting an Algorithm10分钟
Comparison of Modeling Algorithms10分钟
9 个练习
Question 2.012分钟
Question 2.022分钟
Question 2.032分钟
Question 2.042分钟
Question 2.052分钟
Question 2.062分钟
Question 2.072分钟
Question 2.085分钟
Data Preparation and Algorithm Selection Quiz30分钟
3
完成时间为 7 小时

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.

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23 个视频 (总计 68 分钟), 12 个阅读材料, 21 个测验
23 个视频
Basics of Decision Trees2分钟
Demo: Building a Decision Tree Model Using the Default Settings7分钟
Decision Trees for Categorical Targets: Classification Trees3分钟
Decision Trees for Interval Targets: Regression Trees2分钟
Improving the Decision Tree Model25
Demo: Modifying the Structure Parameters1分钟
Recursive Partitioning3分钟
Splitting Criteria4分钟
Split Search9分钟
Demo: Modifying the Recursive Partitioning Parameters1分钟
Optimizing the Complexity of a Decision Tree Model39
Pruning3分钟
Demo: Modifying the Pruning Parameters2分钟
Regularizing and Tuning the Hyperparameters of a Machine Learning Model2分钟
Building Ensemble Models1分钟
Perturb and Combine Methods5分钟
Bagging2分钟
Boosting1分钟
Comparison of Tree-Based Models1分钟
Demo: Building a Gradient Boosting Model3分钟
Forest Models3分钟
Demo: Building a Forest Model4分钟
12 个阅读材料
Impurity Reduction Measures for Categorical and Interval Targets10分钟
Splitting Criteria in Model Studio10分钟
Adjustments in a Split Search10分钟
Missing Values in Decision Trees in Model Studio10分钟
Surrogate Splits10分钟
Calculating Variable Importance for Surrogate Splits10分钟
Bottom-Up Pruning Requirements10分钟
Pruning Options in Model Studio10分钟
Autotuning Options for Decision Trees in Model Studio10分钟
Gradient Boosting Models10分钟
Autotuning Options for Gradient Boosting in Model Studio10分钟
Autotuning Options for Forests in Model Studio10分钟
11 个练习
Question 3.01
Question 3.022分钟
Question 3.032分钟
Question 3.042分钟
Question 3.052分钟
Think About It2分钟
Question 3.062分钟
Question 3.072分钟
Question 3.08
Question 3.092分钟
Decision Trees and Ensembles of Trees Quiz30分钟
4
完成时间为 4 小时

Neural Networks

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

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18 个视频 (总计 37 分钟), 10 个阅读材料, 13 个测验
18 个视频
Beyond Traditional Regression: Neural Networks3分钟
Limitations of Neural Networks2分钟
Basics of Neural Networks3分钟
Estimating Weights and Making Predictions3分钟
Learning Process2分钟
Essential Discovery Tasks for Neural Networks24
Demo: Building a Neural Network Using the Default Settings3分钟
Improving the Neural Network Model22
Neural Network Architectures4分钟
Activation Functions1分钟
Shaping the Sigmoid2分钟
Demo: Modifying the Neural Network Architecture1分钟
Optimizing the Complexity of a Neural Network Model40
Weight Decay1分钟
Early Stopping2分钟
Regularizing and Tuning the Hyperparameters of a Neural Network Model32
Demo: Modifying the Learning and Optimization Parameters2分钟
10 个阅读材料
Standardization Methods10分钟
Iterative Updating in Numerical Optimization10分钟
Numerical Optimization Methods in Model Studio10分钟
Deviance Measures in Model Studio10分钟
Calculating the Number of Parameters10分钟
Deep Learning10分钟
Hidden Layer Activation Functions in Model Studio10分钟
Target Layer Activation Functions and Error Functions in Model Studio10分钟
Selected Hyperparameters Related to the Learning Process in Model Studio10分钟
Autotuning Options for Neural Networks in Model Studio10分钟
8 个练习
Question 4.012分钟
Question 4.022分钟
Question 4.032分钟
Question 4.042分钟
Question 4.052分钟
Question 4.062分钟
Question 4.072分钟
Neural Networks Quiz30分钟

讲师

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Jeff Thompson

Senior Analytical Training Consultant
Education
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Catherine Truxillo

Director, Analytical Education
Education

关于 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....

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