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.
제공자:
Sas Viya를 사용하는 머신 러닝
SAS
About this Course
최근 조회 89,851회
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 27시간 필요
권장: 9 hours/week
영어
자막: 영어
강의 계획 - 이 강좌에서 배울 내용
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 readings, 1 quiz
1개의 동영상
3개의 읽기 자료
Learner Prerequisites
Using SAS® Viya® for Learners with This Course (Required)10m
Using Forums and Getting Help10m
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 40분), 16 readings, 10 quizzes
15개의 동영상
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 1.012m
Question 1.022m
Question 1.032m
Question 1.042m
Question 1.052m
Question 1.062m
Getting Started with Machine Learning and SAS Viya30m
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 47분), 11 readings, 16 quizzes
14개의 동영상
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 2.012m
Question 2.022m
Question 2.032m
Question 2.042m
Question 2.052m
Question 2.062m
Question 2.072m
Question 2.085m
Data Preparation and Algorithm Selection Quiz30m
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 68분), 12 readings, 21 quizzes
23개의 동영상
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 3.022m
Question 3.032m
Question 3.042m
Question 3.052m
Think About It2m
Question 3.062m
Question 3.072m
Question 3.08
Question 3.092m
Decision Trees and Ensembles of Trees Quiz30m
완료하는 데 4시간 필요
Neural Networks
In this module, you learn to build neural network models.
완료하는 데 4시간 필요
18개 동영상 (총 37분), 10 readings, 13 quizzes
18개의 동영상
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 4.012m
Question 4.022m
Question 4.032m
Question 4.042m
Question 4.052m
Question 4.062m
Question 4.072m
Neural Networks Quiz30m
검토
4.7
SAS VIYA를 사용하는 머신 러닝의 최상위 리뷰
TA 제공Jul 5, 2019
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.
SS 제공Mar 13, 2020
This is a good course . SAS is the leader in the world of analytics.
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.
자주 묻는 질문
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강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
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수료증을 구매하면 성적 평가 과제를 포함한 모든 강좌 자료에 접근할 수 있습니다. 강좌를 완료하면 전자 수료증이 성취도 페이지에 추가되며, 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 콘텐츠만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
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