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.
Machine Learning Using SAS Viya
Machine Learning Using SAS Viya
本课程是 SAS Machine Learning Engineer 专业证书 的一部分
位教师：Jeff Thompson
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4.6 (17个评价)
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作业
41 项作业
授课语言：英语（English）
了解顶级公司的员工如何掌握热门技能
积累 Machine Learning 领域的专业知识
本课程是 SAS Machine Learning Engineer 专业证书 专项课程的一部分
在注册此课程时，您还会同时注册此专业证书。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 通过 SAS 获得可共享的职业证书
该课程共有9个模块
In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.
涵盖的内容
1个视频4篇阅读材料1个应用程序项目
1个视频• 总计2分钟
- Welcome to the Course!• 2分钟
4篇阅读材料• 总计25分钟
- Learner Prerequisites• 0分钟
- Using Forums and Getting Help• 5分钟
- Using SAS® Viya® for Learners with This Course (Required)• 10分钟
- Frequently Asked Questions• 10分钟
1个应用程序项目• 总计5分钟
- Access SAS Viya for Learners• 5分钟
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.
涵盖的内容
13个视频17篇阅读材料6个作业3个应用程序项目
13个视频• 总计43分钟
- Introduction• 0分钟
- Machine Learning in SAS Viya• 2分钟
- Analytics Life Cycle• 1分钟
- Case Study: Customer Churn• 2分钟
- SAS Viya Tools for Machine Learning• 2分钟
- Demo: Creating a Project and Loading Data• 6分钟
- Predictive Modeling• 5分钟
- Data Preparation and Preprocessing• 3分钟
- Dividing the Data• 4分钟
- Addressing Rare Events Using Event-Based Sampling• 3分钟
- Demo: Modifying the Data Partition• 6分钟
- Managing Missing Values• 3分钟
- Demo: Building a Pipeline from a Basic Template• 5分钟
17篇阅读材料• 总计175分钟
- Applications of Prediction-Based Decision Making• 10分钟
- Case Study: Data Dictionary• 10分钟
- SAS Viya Applications Menu• 10分钟
- SAS Drive• 15分钟
- SAS Viya Tools for Data Preparation• 10分钟
- Cross Validation for Small Data Sets• 10分钟
- Selecting Variables on the Data Tab• 10分钟
- Global Metadata• 10分钟
- Managing Missing Values: Details• 10分钟
- Imputation Methods in Model Studio• 10分钟
- Automated Pipeline Creation• 10分钟
- Pipeline Templates in Model Studio• 10分钟
- Logistic Regression• 10分钟
- SAS Cloud Analytic Services• 10分钟
- SAS Viya: A Shift in Mindset• 10分钟
- Data Sources and CAS• 10分钟
- Interfaces and Products• 10分钟
6个作业• 总计55分钟
- Question - Model Studio• 5分钟
- Question - Data Partitions• 5分钟
- Question - Missing Values• 5分钟
- Question - Pipeline Templates• 5分钟
- Question - Caslibs• 5分钟
- Getting Started with Machine Learning and SAS Viya Review Quiz• 30分钟
3个应用程序项目• 总计15分钟
- Practice - Creating a Project and Loading Data• 5分钟
- Practice - Modifying the Data Partition• 5分钟
- Practice - Building a Pipeline from a Basic Template• 5分钟
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.
涵盖的内容
14个视频12篇阅读材料7个作业7个应用程序项目
14个视频• 总计44分钟
- Introduction to Data Preprocessing and Algorithm Selection• 1分钟
- Exploring the Data• 1分钟
- Demo: Exploring the Data• 5分钟
- Replacing Incorrect Values• 1分钟
- Demo: Replacing Incorrect Values Starting on the Data Tab• 6分钟
- Feature Extraction• 0分钟
- Text Mining• 2分钟
- Demo: Adding Text Mining Features• 6分钟
- Using Transformations to Handle Extreme or Unusual Values• 4分钟
- Demo: Transforming Inputs• 5分钟
- Selecting Useful Inputs• 4分钟
- Demo: Selecting Features• 6分钟
- Demo: Saving a Pipeline to the Exchange• 2分钟
- Starting the Discovery Phase of the Analytics Life Cycle• 1分钟
12篇阅读材料• 总计120分钟
- Data Preprocessing Nodes in Model Studio• 10分钟
- Risk Modeling Add-on for SAS Viya• 10分钟
- Replacing Incorrect Values Starting with the Manage Variables Node• 10分钟
- Singular Value Decomposition and the Text Mining Node• 10分钟
- Feature Extraction Node• 10分钟
- Transformations in Model Studio• 10分钟
- Feature Selection and the Variable Selection Node: Details• 10分钟
- Best Practices for Common Data Preparation Challenges• 10分钟
- Feature Engineering in SAS Viya• 10分钟
- Supervised Learning Algorithms in Model Studio• 10分钟
- Considerations for Selecting an Algorithm• 10分钟
- Comparison of Modeling Algorithms• 10分钟
7个作业• 总计60分钟
- Question - Data Exploration Node• 5分钟
- Question - Text Mining Node• 5分钟
- Question - Transformations• 5分钟
- Question - Variable Selection Node• 5分钟
- Question - Data Collection Challenges• 5分钟
- Question - Algorithm Selection• 5分钟
- Data Preprocessing and Algorithm Selection Review Quiz• 30分钟
7个应用程序项目• 总计65分钟
- Practice - Exploring the Data• 5分钟
- Practice - Replacing Incorrect Values Starting on the Data Tab• 5分钟
- Practice - Adding Text Mining Features• 5分钟
- Practice - Transforming Inputs• 5分钟
- Practice - Selecting Features• 5分钟
- Practice - Saving a Pipeline to the Exchange• 20分钟
- Practice - Saving a Pipeline to the Exchange• 20分钟
In this module, you learn to build decision tree models as well as models based on ensembles, or combinations, of decision trees.
涵盖的内容
23个视频17篇阅读材料11个作业7个应用程序项目
23个视频• 总计71分钟
- Introduction to Decision Trees and Ensembles of Trees• 1分钟
- Basics of Decision Trees• 3分钟
- Demo: Building a Decision Tree Model Using the Default Settings• 9分钟
- Decision Trees for Categorical Targets: Classification Trees• 4分钟
- Decision Trees for Interval Targets: Regression Trees• 2分钟
- Improving the Decision Tree Model• 0分钟
- Demo: Modifying the Structure Parameters• 2分钟
- Recursive Partitioning• 3分钟
- Splitting Criteria• 4分钟
- Split Search• 10分钟
- Demo: Modifying the Recursive Partitioning Parameters• 1分钟
- Optimizing the Complexity of a Decision Tree Model• 1分钟
- Pruning• 3分钟
- Demo: Modifying the Pruning Parameters• 2分钟
- Regularizing and Tuning the Hyperparameters of a Machine Learning Model• 3分钟
- Building Ensemble Models• 1分钟
- Perturb and Combine Methods• 6分钟
- Bagging• 2分钟
- Boosting• 1分钟
- Comparison of Tree-Based Models• 1分钟
- Demo: Building a Gradient Boosting Model• 4分钟
- Forest Models• 3分钟
- Demo: Building a Forest Model• 4分钟
17篇阅读材料• 总计170分钟
- Supervised Learning Node Results Window• 10分钟
- Score Code in Model Studio• 10分钟
- Interactively Edit a Decision Tree• 10分钟
- Impurity Reduction Measures for Categorical and Interval Targets• 10分钟
- Splitting Criteria in Model Studio• 10分钟
- Adjustments in a Split Search• 10分钟
- Missing Values in Decision Trees in Model Studio• 10分钟
- Surrogate Splits• 10分钟
- Calculating Variable Importance for Surrogate Splits• 10分钟
- Bottom-Up Pruning Requirements• 10分钟
- Pruning Options in Model Studio• 10分钟
- Hyperparameter Optimization Approaches• 10分钟
- Autotuning Options for Decision Trees in Model Studio• 10分钟
- Use the Best Hyperparameter Values for a Model• 10分钟
- Gradient Boosting Models• 10分钟
- Autotuning Options for Gradient Boosting in Model Studio• 10分钟
- Autotuning Options for Forests in Model Studio• 10分钟
11个作业• 总计80分钟
- Question - Decision Tree Nodes• 5分钟
- Question - Decision Tree Splits• 5分钟
- Question - Decision Trees• 5分钟
- Question - Leaf Purity• 5分钟
- Question - Number of Splits• 5分钟
- Think about it• 5分钟
- Question - Pruning• 5分钟
- Question - Perturb and Combine Methods• 5分钟
- Question Tree-Based Models• 5分钟
- Question - Gradient Boosting versus Forest Models• 5分钟
- Decision Trees and Ensembles of Trees Review Quiz• 30分钟
7个应用程序项目• 总计140分钟
- Practice - Building a Decision Tree Using the Default Settings• 20分钟
- Practice - Modifying the Structure Parameters• 20分钟
- Practice - Modifying the Recursive Partitioning Parameters• 20分钟
- Practice - Modifying the Pruning Parameters• 20分钟
- Practice - Modifying the Pruning Parameters• 20分钟
- Practice - Building a Gradient Boosting Model• 20分钟
- Practice - Building a Forest Model• 20分钟
In this module, you learn to build neural network models.
涵盖的内容
19个视频11篇阅读材料6个作业3个应用程序项目
19个视频• 总计42分钟
- Introduction to Neural Networks• 0分钟
- Beyond Traditional Regression: Neural Networks• 4分钟
- Overcoming the Limitations of Neural Networks• 3分钟
- Basics of Neural Networks• 4分钟
- Estimating Weights and Making Predictions• 3分钟
- Learning Process• 2分钟
- Essential Discovery Tasks for Neural Networks• 0分钟
- Demo: Building a Neural Network Using the Default Settings• 3分钟
- Improving the Neural Network Model• 0分钟
- Neural Network Architectures• 4分钟
- Activation Functions• 2分钟
- Shaping the Sigmoid• 2分钟
- Demo: Modifying the Neural Network Architecture• 2分钟
- Optimizing the Complexity of a Neural Network Model• 1分钟
- Weight Decay• 3分钟
- Early Stopping• 3分钟
- Regularizing and Tuning the Hyperparameters of a Neural Network Model• 1分钟
- Network Learning Hyperparameters• 3分钟
- Demo: Modifying the Learning and Optimization Parameters• 2分钟
11篇阅读材料• 总计110分钟
- Standardization Methods• 10分钟
- Iterative Updating in Numerical Optimization• 10分钟
- Numerical Optimization Methods in Model Studio• 10分钟
- Deviance Measures in Model Studio• 10分钟
- Stationary Versus Nonstationary Data• 10分钟
- Calculating the Number of Parameters• 10分钟
- Deep Learning• 10分钟
- Hidden Layer Activation Functions in Model Studio• 10分钟
- Target Layer Activation Functions and Error Functions in Model Studio• 10分钟
- Important Hyperparameters for Neural Networks: Summary• 10分钟
- Autotuning Options for Neural Networks in Model Studio• 10分钟
6个作业• 总计65分钟
- Question - Universal Approximators• 5分钟
- Question - Parameter and Intercept Estimates• 10分钟
- Question - Optimization Methods• 5分钟
- Question - Deep Learning Models• 5分钟
- Question - Model Generalization and Early Stopping• 10分钟
- Neural Networks Review Quiz• 30分钟
3个应用程序项目• 总计60分钟
- Practice - Building a Neural Network Using the Default Settings• 20分钟
- Practice - Modifying the Neural Network Architecture• 20分钟
- Practice - Modifying the Learning and Optimization Parameters• 20分钟
In this module, you learn to build support vector machine models.
涵盖的内容
17个视频7篇阅读材料5个作业4个应用程序项目
17个视频• 总计32分钟
- Introduction to Support Vector Machines• 1分钟
- Support Vector Machines as Classifier Models• 2分钟
- Mathematical Definition of a Support Vector Machine• 2分钟
- Maximum-Margin Hyperplane and Support Vectors• 2分钟
- Essential Discovery Tasks for Support Vector Machines• 0分钟
- Demo: Building a Support Vector Machine Using the Default Settings• 3分钟
- Improving the Support Vector Machine Model• 1分钟
- Optimization Problem• 1分钟
- Accounting for Errors with Nonlinearly Separable Data• 1分钟
- Demo: Modifying the Methods of Solution Parameters• 1分钟
- Optimizing the Complexity of the Support Vector Machine Model• 0分钟
- Feature Space Approach for Nonlinearly Separable Data• 1分钟
- Kernel Trick• 2分钟
- Demo: Increasing the Flexibility of the Support Vector Machine• 2分钟
- Model Interpretability• 1分钟
- Demo: Adding Model Interpretability• 11分钟
- Regularizing and Tuning the Hyperparameters of the Support Vector Machine Model• 0分钟
7篇阅读材料• 总计70分钟
- Dot Products• 10分钟
- Constraints for Optimization• 10分钟
- Number of Support Vectors• 10分钟
- Lagrange Approach for Estimation• 10分钟
- SVM Properties Related to Kernels and Training Algorithms• 10分钟
- Model Interpretability Plots• 10分钟
- Autotuning Options for Support Vector Machines in Model Studio• 10分钟
5个作业• 总计50分钟
- Question - SVM and the Curse of Dimensionality• 5分钟
- Question - Non-linearly Separable Data• 5分钟
- Question - Kernel Functions• 5分钟
- Question - Model Interpretability• 5分钟
- Support Vector Machines Review Quiz• 30分钟
4个应用程序项目• 总计80分钟
- Practice - Building a Support Vector Machine Using the Default Settings• 20分钟
- Practice - Modifying the Methods of Solution Parameters• 20分钟
- Practice - Increasing the Flexibility of the Support Vector Machine• 20分钟
- Practice - Adding Model Interpretability• 20分钟
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.
涵盖的内容
17个视频8篇阅读材料6个作业7个应用程序项目
17个视频• 总计37分钟
- Introduction to Model Deployment• 0分钟
- Essential Deployment Tasks• 1分钟
- Selecting a Model• 1分钟
- Numeric Measures of Model Performance• 1分钟
- Confusion Matrix for Decision Predictions• 2分钟
- ROC Charts and the C-Statistic• 4分钟
- Charts Based on Response Rate: CPH and Lift• 4分钟
- Ways of Comparing Models in Model Studio• 1分钟
- Demo: Comparing Models within a Pipeline• 3分钟
- Demo: Comparing Models across Pipelines• 3分钟
- Demo: Reviewing a Project Summary Report on the Insights Tab• 4分钟
- Demo: Registering the Champion Model• 1分钟
- Demo: Exploring the Settings for Model Selection• 3分钟
- Scoring and Managing the Champion Model• 1分钟
- Demo: Viewing the Score Code and Running a Scoring Test• 7分钟
- Assessing Model Bias• 1分钟
- Monitoring and Updating the Model• 2分钟
8篇阅读材料• 总计80分钟
- Numeric Measures of Model Performance by Prediction Type• 10分钟
- Score Holdout Data (Out-of-Time Testing)• 10分钟
- Model Selection Statistics by Target Type• 10分钟
- Score Code and Model Deployment• 10分钟
- Internally Scored Data Sets in Model Studio• 10分钟
- Introducing SAS Model Manager• 10分钟
- SAS Model Manager Tabs• 10分钟
- Assessing Model Bias and Relevant Resources• 10分钟
6个作业• 总计55分钟
- Question - C-Statistic• 5分钟
- Question - Model Comparison Node• 5分钟
- Question - Model Selection Statistics• 5分钟
- Question - Model Comparison Node Properties• 5分钟
- Question - Scoring• 5分钟
- Model Assessment and Deployment Review Quiz• 30分钟
7个应用程序项目• 总计140分钟
- Practice - Comparing Models Within a Pipeline• 20分钟
- Practice - Comparing Models Across Pipelines• 20分钟
- Practice - Reviewing a Project Summary Report on the Insights Tab• 20分钟
- Practice - Registering the Champion Model• 20分钟
- Practice - Registering the Champion Model• 20分钟
- Practice - Exploring the Settings for Model Selection• 20分钟
- Practice - Viewing the Score Code and Running a Scoring Test• 20分钟
涵盖的内容
2个视频4篇阅读材料1个应用程序项目
2个视频• 总计8分钟
- Introduction to Additional Nodes• 1分钟
- Demo: Adding Open Source Models to a Model Studio Project• 6分钟
4篇阅读材料• 总计40分钟
- Additional Nodes in Model Studio• 10分钟
- Save Data Node• 10分钟
- SAS Code Node• 10分钟
- Open Source Code Node• 10分钟
1个应用程序项目• 总计20分钟
- Practice - Adding Open Source Models to a Model Studio Project• 20分钟
涵盖的内容
1个应用程序项目
1个应用程序项目• 总计60分钟
- Certification Practice Exam: Machine Learning Specialist• 60分钟
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位教师
授课教师评分
4.6 (17个评价)授课教师评分
我们要求所有学生根据授课教师的教学风格和质量提供对授课教师的反馈。
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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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学生评论
4.7
102 条评论
- 5 stars
76.47%
- 4 stars
17.64%
- 3 stars
5.88%
- 2 stars
0%
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显示 3/102 个
R
RM
5·
已于 Apr 7, 2020审阅
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.
S
SS
5·
已于 Mar 13, 2020审阅
This is a good course . SAS is the leader in the world of analytics.
K
KK
4·
已于 May 11, 2020审阅
nice course for those who already knew machine learning concepts and wants to learn SAS viya for creating models.
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