Machine Learning Using SAS Viya

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Machine Learning Using SAS Viya

Dozenten: Jeff Thompson

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9 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

4.7

(102 Bewertungen)

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9 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

4.7

(102 Bewertungen)

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Einige einschlägige Kenntnisse erforderlich

3 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Kompetenzen, die Sie erwerben

Kategorie: Predictive Analytics
Kategorie: Artificial Neural Networks
Kategorie: Data Analysis
Kategorie: Applied Machine Learning
Kategorie: Machine Learning
Kategorie: Predictive Modeling
Kategorie: Decision Tree Learning
Kategorie: Feature Engineering
Kategorie: SAS (Software)
Kategorie: Data Cleansing
Kategorie: Statistical Programming
Kategorie: Supervised Learning

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41 Aufgaben

Unterrichtet in Englisch

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

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In diesem Kurs gibt es 9 Module

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.

In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.

Das ist alles enthalten

1 Video4 Lektüren1 App-Element

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.

Das ist alles enthalten

13 Videos17 Lektüren6 Aufgaben3 App-Elemente

13 VideosInsgesamt 42 Minuten

Introduction0 Minuten
Machine Learning in SAS Viya2 Minuten
Analytics Life Cycle1 Minute
Case Study: Customer Churn2 Minuten
SAS Viya Tools for Machine Learning1 Minute
Demo: Creating a Project and Loading Data6 Minuten
Predictive Modeling5 Minuten
Data Preparation and Preprocessing2 Minuten
Dividing the Data3 Minuten
Addressing Rare Events Using Event-Based Sampling3 Minuten
Demo: Modifying the Data Partition5 Minuten
Managing Missing Values3 Minuten
Demo: Building a Pipeline from a Basic Template5 Minuten

17 LektürenInsgesamt 175 Minuten

Applications of Prediction-Based Decision Making10 Minuten
Case Study: Data Dictionary10 Minuten
SAS Viya Applications Menu10 Minuten
SAS Drive15 Minuten
SAS Viya Tools for Data Preparation10 Minuten
Cross Validation for Small Data Sets10 Minuten
Selecting Variables on the Data Tab10 Minuten
Global Metadata10 Minuten
Managing Missing Values: Details10 Minuten
Imputation Methods in Model Studio10 Minuten
Automated Pipeline Creation10 Minuten
Pipeline Templates in Model Studio10 Minuten
Logistic Regression10 Minuten
SAS Cloud Analytic Services10 Minuten
SAS Viya: A Shift in Mindset10 Minuten
Data Sources and CAS10 Minuten
Interfaces and Products10 Minuten

6 AufgabenInsgesamt 55 Minuten

Getting Started with Machine Learning and SAS Viya Review Quiz30 Minuten
Question - Model Studio5 Minuten
Question - Data Partitions5 Minuten
Question - Missing Values5 Minuten
Question - Pipeline Templates5 Minuten
Question - Caslibs5 Minuten

3 App-ElementeInsgesamt 15 Minuten

Practice - Creating a Project and Loading Data5 Minuten
Practice - Modifying the Data Partition5 Minuten
Practice - Building a Pipeline from a Basic Template5 Minuten

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.

Das ist alles enthalten

14 Videos12 Lektüren7 Aufgaben7 App-Elemente

14 VideosInsgesamt 43 Minuten

Introduction to Data Preprocessing and Algorithm Selection0 Minuten
Exploring the Data1 Minute
Demo: Exploring the Data4 Minuten
Replacing Incorrect Values1 Minute
Demo: Replacing Incorrect Values Starting on the Data Tab6 Minuten
Feature Extraction0 Minuten
Text Mining1 Minute
Demo: Adding Text Mining Features6 Minuten
Using Transformations to Handle Extreme or Unusual Values3 Minuten
Demo: Transforming Inputs5 Minuten
Selecting Useful Inputs4 Minuten
Demo: Selecting Features5 Minuten
Demo: Saving a Pipeline to the Exchange1 Minute
Starting the Discovery Phase of the Analytics Life Cycle1 Minute

12 LektürenInsgesamt 120 Minuten

Data Preprocessing Nodes in Model Studio10 Minuten
Risk Modeling Add-on for SAS Viya10 Minuten
Replacing Incorrect Values Starting with the Manage Variables Node10 Minuten
Singular Value Decomposition and the Text Mining Node10 Minuten
Feature Extraction Node10 Minuten
Transformations in Model Studio10 Minuten
Feature Selection and the Variable Selection Node: Details10 Minuten
Best Practices for Common Data Preparation Challenges10 Minuten
Feature Engineering in SAS Viya10 Minuten
Supervised Learning Algorithms in Model Studio10 Minuten
Considerations for Selecting an Algorithm10 Minuten
Comparison of Modeling Algorithms10 Minuten

7 AufgabenInsgesamt 60 Minuten

Data Preprocessing and Algorithm Selection Review Quiz30 Minuten
Question - Data Exploration Node5 Minuten
Question - Text Mining Node5 Minuten
Question - Transformations5 Minuten
Question - Variable Selection Node5 Minuten
Question - Data Collection Challenges5 Minuten
Question - Algorithm Selection5 Minuten

7 App-ElementeInsgesamt 65 Minuten

Practice - Exploring the Data5 Minuten
Practice - Replacing Incorrect Values Starting on the Data Tab5 Minuten
Practice - Adding Text Mining Features5 Minuten
Practice - Transforming Inputs5 Minuten
Practice - Selecting Features5 Minuten
Practice - Saving a Pipeline to the Exchange20 Minuten
Practice - Saving a Pipeline to the Exchange20 Minuten

In this module, you learn to build decision tree models as well as models based on ensembles, or combinations, of decision trees.

Das ist alles enthalten

23 Videos17 Lektüren11 Aufgaben7 App-Elemente

23 VideosInsgesamt 70 Minuten

Introduction to Decision Trees and Ensembles of Trees0 Minuten
Basics of Decision Trees2 Minuten
Demo: Building a Decision Tree Model Using the Default Settings9 Minuten
Decision Trees for Categorical Targets: Classification Trees3 Minuten
Decision Trees for Interval Targets: Regression Trees2 Minuten
Improving the Decision Tree Model0 Minuten
Demo: Modifying the Structure Parameters2 Minuten
Recursive Partitioning3 Minuten
Splitting Criteria4 Minuten
Split Search9 Minuten
Demo: Modifying the Recursive Partitioning Parameters1 Minute
Optimizing the Complexity of a Decision Tree Model0 Minuten
Pruning3 Minuten
Demo: Modifying the Pruning Parameters2 Minuten
Regularizing and Tuning the Hyperparameters of a Machine Learning Model3 Minuten
Building Ensemble Models1 Minute
Perturb and Combine Methods5 Minuten
Bagging2 Minuten
Boosting1 Minute
Comparison of Tree-Based Models1 Minute
Demo: Building a Gradient Boosting Model4 Minuten
Forest Models3 Minuten
Demo: Building a Forest Model4 Minuten

17 LektürenInsgesamt 170 Minuten

Supervised Learning Node Results Window10 Minuten
Score Code in Model Studio10 Minuten
Interactively Edit a Decision Tree10 Minuten
Impurity Reduction Measures for Categorical and Interval Targets10 Minuten
Splitting Criteria in Model Studio10 Minuten
Adjustments in a Split Search10 Minuten
Missing Values in Decision Trees in Model Studio10 Minuten
Surrogate Splits10 Minuten
Calculating Variable Importance for Surrogate Splits10 Minuten
Bottom-Up Pruning Requirements10 Minuten
Pruning Options in Model Studio10 Minuten
Hyperparameter Optimization Approaches10 Minuten
Autotuning Options for Decision Trees in Model Studio10 Minuten
Use the Best Hyperparameter Values for a Model10 Minuten
Gradient Boosting Models10 Minuten
Autotuning Options for Gradient Boosting in Model Studio10 Minuten
Autotuning Options for Forests in Model Studio10 Minuten

11 AufgabenInsgesamt 80 Minuten

Decision Trees and Ensembles of Trees Review Quiz30 Minuten
Question - Decision Tree Nodes5 Minuten
Question - Decision Tree Splits5 Minuten
Question - Decision Trees5 Minuten
Question - Leaf Purity5 Minuten
Question - Number of Splits5 Minuten
Think about it5 Minuten
Question - Pruning5 Minuten
Question - Perturb and Combine Methods5 Minuten
Question Tree-Based Models5 Minuten
Question - Gradient Boosting versus Forest Models5 Minuten

7 App-ElementeInsgesamt 140 Minuten

Practice - Building a Decision Tree Using the Default Settings20 Minuten
Practice - Modifying the Structure Parameters20 Minuten
Practice - Modifying the Recursive Partitioning Parameters20 Minuten
Practice - Modifying the Pruning Parameters20 Minuten
Practice - Modifying the Pruning Parameters20 Minuten
Practice - Building a Gradient Boosting Model20 Minuten
Practice - Building a Forest Model20 Minuten

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

Das ist alles enthalten

19 Videos11 Lektüren6 Aufgaben3 App-Elemente

19 VideosInsgesamt 42 Minuten

Introduction to Neural Networks0 Minuten
Beyond Traditional Regression: Neural Networks4 Minuten
Overcoming the Limitations of Neural Networks3 Minuten
Basics of Neural Networks3 Minuten
Estimating Weights and Making Predictions3 Minuten
Learning Process2 Minuten
Essential Discovery Tasks for Neural Networks0 Minuten
Demo: Building a Neural Network Using the Default Settings2 Minuten
Improving the Neural Network Model0 Minuten
Neural Network Architectures4 Minuten
Activation Functions1 Minute
Shaping the Sigmoid2 Minuten
Demo: Modifying the Neural Network Architecture1 Minute
Optimizing the Complexity of a Neural Network Model0 Minuten
Weight Decay2 Minuten
Early Stopping2 Minuten
Regularizing and Tuning the Hyperparameters of a Neural Network Model0 Minuten
Network Learning Hyperparameters3 Minuten
Demo: Modifying the Learning and Optimization Parameters2 Minuten

11 LektürenInsgesamt 110 Minuten

Standardization Methods10 Minuten
Iterative Updating in Numerical Optimization10 Minuten
Numerical Optimization Methods in Model Studio10 Minuten
Deviance Measures in Model Studio10 Minuten
Stationary Versus Nonstationary Data10 Minuten
Calculating the Number of Parameters10 Minuten
Deep Learning10 Minuten
Hidden Layer Activation Functions in Model Studio10 Minuten
Target Layer Activation Functions and Error Functions in Model Studio10 Minuten
Important Hyperparameters for Neural Networks: Summary10 Minuten
Autotuning Options for Neural Networks in Model Studio10 Minuten

6 AufgabenInsgesamt 65 Minuten

Neural Networks Review Quiz30 Minuten
Question - Universal Approximators5 Minuten
Question - Parameter and Intercept Estimates10 Minuten
Question - Optimization Methods5 Minuten
Question - Deep Learning Models5 Minuten
Question - Model Generalization and Early Stopping10 Minuten

3 App-ElementeInsgesamt 60 Minuten

Practice - Building a Neural Network Using the Default Settings20 Minuten
Practice - Modifying the Neural Network Architecture20 Minuten
Practice - Modifying the Learning and Optimization Parameters20 Minuten

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

Das ist alles enthalten

17 Videos7 Lektüren5 Aufgaben4 App-Elemente

17 VideosInsgesamt 32 Minuten

Introduction to Support Vector Machines0 Minuten
Support Vector Machines as Classifier Models2 Minuten
Mathematical Definition of a Support Vector Machine1 Minute
Maximum-Margin Hyperplane and Support Vectors1 Minute
Essential Discovery Tasks for Support Vector Machines0 Minuten
Demo: Building a Support Vector Machine Using the Default Settings2 Minuten
Improving the Support Vector Machine Model1 Minute
Optimization Problem1 Minute
Accounting for Errors with Nonlinearly Separable Data1 Minute
Demo: Modifying the Methods of Solution Parameters1 Minute
Optimizing the Complexity of the Support Vector Machine Model0 Minuten
Feature Space Approach for Nonlinearly Separable Data1 Minute
Kernel Trick1 Minute
Demo: Increasing the Flexibility of the Support Vector Machine2 Minuten
Model Interpretability1 Minute
Demo: Adding Model Interpretability11 Minuten
Regularizing and Tuning the Hyperparameters of the Support Vector Machine Model0 Minuten

7 LektürenInsgesamt 70 Minuten

Dot Products10 Minuten
Constraints for Optimization10 Minuten
Number of Support Vectors10 Minuten
Lagrange Approach for Estimation10 Minuten
SVM Properties Related to Kernels and Training Algorithms10 Minuten
Model Interpretability Plots10 Minuten
Autotuning Options for Support Vector Machines in Model Studio10 Minuten

5 AufgabenInsgesamt 50 Minuten

Support Vector Machines Review Quiz30 Minuten
Question - SVM and the Curse of Dimensionality5 Minuten
Question - Non-linearly Separable Data5 Minuten
Question - Kernel Functions5 Minuten
Question - Model Interpretability5 Minuten

4 App-ElementeInsgesamt 80 Minuten

Practice - Building a Support Vector Machine Using the Default Settings20 Minuten
Practice - Modifying the Methods of Solution Parameters20 Minuten
Practice - Increasing the Flexibility of the Support Vector Machine20 Minuten
Practice - Adding Model Interpretability20 Minuten

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.

Das ist alles enthalten

17 Videos8 Lektüren6 Aufgaben7 App-Elemente

17 VideosInsgesamt 36 Minuten

Introduction to Model Deployment0 Minuten
Essential Deployment Tasks0 Minuten
Selecting a Model0 Minuten
Numeric Measures of Model Performance1 Minute
Confusion Matrix for Decision Predictions1 Minute
ROC Charts and the C-Statistic3 Minuten
Charts Based on Response Rate: CPH and Lift3 Minuten
Ways of Comparing Models in Model Studio1 Minute
Demo: Comparing Models within a Pipeline2 Minuten
Demo: Comparing Models across Pipelines2 Minuten
Demo: Reviewing a Project Summary Report on the Insights Tab3 Minuten
Demo: Registering the Champion Model0 Minuten
Demo: Exploring the Settings for Model Selection2 Minuten
Scoring and Managing the Champion Model0 Minuten
Demo: Viewing the Score Code and Running a Scoring Test7 Minuten
Assessing Model Bias0 Minuten
Monitoring and Updating the Model1 Minute

8 LektürenInsgesamt 80 Minuten

Numeric Measures of Model Performance by Prediction Type10 Minuten
Score Holdout Data (Out-of-Time Testing)10 Minuten
Model Selection Statistics by Target Type10 Minuten
Score Code and Model Deployment10 Minuten
Internally Scored Data Sets in Model Studio10 Minuten
Introducing SAS Model Manager10 Minuten
SAS Model Manager Tabs10 Minuten
Assessing Model Bias and Relevant Resources10 Minuten

6 AufgabenInsgesamt 55 Minuten

Model Assessment and Deployment Review Quiz30 Minuten
Question - C-Statistic5 Minuten
Question - Model Comparison Node5 Minuten
Question - Model Selection Statistics5 Minuten
Question - Model Comparison Node Properties5 Minuten
Question - Scoring5 Minuten

7 App-ElementeInsgesamt 140 Minuten

Practice - Comparing Models Within a Pipeline20 Minuten
Practice - Comparing Models Across Pipelines20 Minuten
Practice - Reviewing a Project Summary Report on the Insights Tab20 Minuten
Practice - Registering the Champion Model20 Minuten
Practice - Registering the Champion Model20 Minuten
Practice - Exploring the Settings for Model Selection20 Minuten
Practice - Viewing the Score Code and Running a Scoring Test20 Minuten

Das ist alles enthalten

2 Videos4 Lektüren1 App-Element

2 VideosInsgesamt 7 Minuten

Introduction to Additional Nodes1 Minute
Demo: Adding Open Source Models to a Model Studio Project6 Minuten

4 LektürenInsgesamt 40 Minuten

Additional Nodes in Model Studio10 Minuten
Save Data Node10 Minuten
SAS Code Node10 Minuten
Open Source Code Node10 Minuten

1 App-ElementInsgesamt 20 Minuten

Practice - Adding Open Source Models to a Model Studio Project20 Minuten

Das ist alles enthalten

1 App-Element

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Dozenten

Lehrkraftbewertungen

4.6 (17 Bewertungen)

Jeff Thompson

SAS

1 Kurs8.768 Lernende

Catherine Truxillo

SAS

1 Kurs8.768 Lernende

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Geprüft am 11. Mai 2020

Thank you very much!!!This course will change my career and life.

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This is a good course . SAS is the leader in the world of analytics.

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The content is great, although the platform failed after 5 days of inactivity. My account was deactivated and I was never able to recover it so I couldn't follow the tutorials.

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