4.7
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97 ratings

Jeff Thompson +1 more instructor

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Machine Learning Using SAS Viya

SAS

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

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Intermediate Level

Approx. 33 hours to complete

English

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Intermediate Level

Approx. 33 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.61/5 (16 Ratings)

Jeff Thompson

Senior Analytical Training Consultant

Education

7,540 Learners

1 Course

Catherine Truxillo

Director, Analytical Education

Education

7,540 Learners

1 Course

Offered by

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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Syllabus - What you will learn from this course

Week1

Week 1

1 hour to complete

Course Overview

1 hour to complete

1 video (Total 2 min), 3 readings, 1 quiz

5 hours to complete

Getting Started with Machine Learning using SAS® Viya

5 hours to complete

15 videos (Total 43 min), 16 readings, 9 quizzes

15 videos

Introduction13sMachine Learning in SAS Viya2mAnalytics Life Cycle1mCase Study: Customer Churn2mSAS Viya Tools for SAS Visual Data Mining and Machine Learning1mDemo: Creating a Project and Selecting Data5mImporting Data from a Local Source1mPredictive Modeling5mData Preparation1mDividing the Data3mAddressing Rare Events Using Event-Based Sampling3mDemo: Modifying the Data Partition5mManaging Missing Values3mDemo: Building a Pipeline from a Basic Template5mSAS Viya in the SAS Platform: Architecture1m

16 readings

Applications of Prediction-Based Decision Making10mCase Study: Data Dictionary10mSAS Drive and the Application Bar10mImporting Data from a Local Source10mSAS Viya Tools for Data Preparation10mCross Validation for Small Data Sets10mSelecting Variables on the Data Tab10mGlobal Metadata10mManaging Missing Values: Details10mImputation Methods in Model Studio10mPipeline Templates in Model Studio10mLogistic Regression10mSAS Cloud Analytic Services10mSAS Viya: A Shift in Mindset10mData Sources and CAS10mInterfaces and Products10m

6 practice exercises

Question 1.025mQuestion 1.035mQuestion 1.045mQuestion 1.055mQuestion 1.065mGetting Started with Machine Learning and SAS Viya30m

Week2

Week 2

6 hours to complete

Data Preparation and Algorithm Selection

6 hours to complete

14 videos (Total 44 min), 12 readings, 16 quizzes

14 videos

Introduction to Data Preparation and Algorithm Selection45sExploring the Data1mDemo: Exploring the Data4mReplacing Incorrect Values1mDemo: Replacing Incorrect Values Starting on the Data Tab6mFeature Creation24sText Mining1mDemo: Adding Text Mining Features6mUsing Transformations to Handle Extreme or Unusual Values3mDemo: Transforming Inputs5mSelecting Useful Inputs4mDemo: Selecting Features5mDemo: Saving a Pipeline to the Exchange1mStarting the Discovery Phase of the Analytics Life Cycle1m

12 readings

Data Mining Preprocessing Nodes in Model Studio10mRisk Modeling Add-on for SAS Visual Data Mining and Machine Learning10mReplacing Incorrect Values Starting with the Manage Variables Node10mSingular Value Decomposition and the Text Mining Node10mFeature Extraction Node10mTransformations in Model Studio10mFeature Selection and the Variable Selection Node: Details10mVariable Clustering10mBest Practices for Common Data Preparation Challenges10mFeature Engineering in SAS Visual Data Mining and Machine Learning10mConsiderations for Selecting an Algorithm10mComparison of Modeling Algorithms10m

8 practice exercises

Question 2.015mQuestion 2.025mQuestion 2.035mQuestion 2.045mQuestion 2.055mQuestion 2.075mQuestion 2.085mData Preparation and Algorithm Selection Quiz30m

Week3

Week 3

7 hours to complete

Decision Trees and Ensembles of Trees

7 hours to complete

23 videos (Total 69 min), 14 readings, 17 quizzes

23 videos

Introduction to Decision Trees and Ensembles of Trees38sBasics of Decision Trees2mDemo: Building a Decision Tree Model Using the Default Settings7mDecision Trees for Categorical Targets: Classification Trees3mDecision Trees for Interval Targets: Regression Trees2mImproving the Decision Tree Model25sDemo: Modifying the Structure Parameters2mRecursive Partitioning3mSplitting Criteria4mSplit Search9mDemo: Modifying the Recursive Partitioning Parameters1mOptimizing the Complexity of a Decision Tree Model36sPruning3mDemo: Modifying the Pruning Parameters2mRegularizing and Tuning the Hyperparameters of a Machine Learning Model2mBuilding Ensemble Models1mPerturb and Combine Methods5mBagging2mBoosting1mComparison of Tree-Based Models1mDemo: Building a Gradient Boosting Model3mForest Models3mDemo: Building a Forest Model4m

14 readings

Score Code in Model Studio10mImpurity Reduction Measures for Categorical and Interval Targets10mSplitting Criteria in Model Studio10mAdjustments in a Split Search10mMissing Values in Decision Trees in Model Studio10mSurrogate Splits10mCalculating Variable Importance for Surrogate Splits10mBottom-Up Pruning Requirements10mPruning Options in Model Studio10mHyperparameter Optimization Methods10mAutotuning Options for Decision Trees in Model Studio10mGradient Boosting Models10mAutotuning Options for Gradient Boosting in Model Studio10mAutotuning Options for Forests in Model Studio10m

10 practice exercises

Question 3.015mQuestion 3.025mQuestion 3.035mQuestion 3.045mQuestion 3.055mQuestion 3.065mQuestion 3.075mQuestion 3.085mQuestion 3.095mDecision Trees and Ensembles of Trees Quiz30m

Week4

Week 4

4 hours to complete

Neural Networks

4 hours to complete

19 videos (Total 42 min), 9 readings, 10 quizzes

19 videos

Introduction to Neural Networks27sBeyond Traditional Regression: Neural Networks4mOvercoming the Limitations of Neural Networks3mBasics of Neural Networks3mEstimating Weights and Making Predictions3mLearning Process2mEssential Discovery Tasks for Neural Networks24sDemo: Building a Neural Network Using the Default Settings2mImproving the Neural Network Model18sNeural Network Architectures4mActivation Functions1mShaping the Sigmoid2mDemo: Modifying the Neural Network Architecture1mOptimizing the Complexity of a Neural Network Model37sWeight Decay2mEarly Stopping2mRegularizing and Tuning the Hyperparameters of a Neural Network Model32sNetwork Learning Hyperparameters3mDemo: Modifying the Learning and Optimization Parameters2m

9 readings

Standardization Methods10mIterative Updating in Numerical Optimization10mNumerical Optimization Methods in Model Studio10mDeviance Measures in Model Studio10mCalculating the Number of Parameters10mDeep Learning10mHidden Layer Activation Functions in Model Studio10mTarget Layer Activation Functions and Error Functions in Model Studio10mAutotuning Options for Neural Networks in Model Studio10m

6 practice exercises

Question 4.015mQuestion 4.02 - 4.0310mQuestion 4.045mQuestion 4.055mQuestion 4.06 - 4.0710mNeural Networks Quiz30m

Week5

Week 5

4 hours to complete

Support Vector Machines

4 hours to complete

17 videos (Total 31 min), 5 readings, 10 quizzes

17 videos

Introduction to Support Vector Machines43sSupport Vector Machines as Classifier Models2mMathematical Definition of a Support Vector Machine1mMaximum-Margin Hyperplane and Support Vectors1mEssential Discovery Tasks for Support Vector Machines9sDemo: Building a Support Vector Machine Using the Default Settings2mImproving the Support Vector Machine Model1mOptimization Problem1mAccounting for Errors with Nonlinearly Separable Data51sDemo: Modifying the Methods of Solution Parameters1mOptimizing the Complexity of the Support Vector Machine Model28sFeature Space Approach for Nonlinearly Separable Data1mKernel Trick1mDemo: Increasing the Flexibility of the Support Vector Machine2mModel Interpretability1mDemo: Adding Model Interpretability10mRegularizing and Tuning the Hyperparameters of the Support Vector Machine Model22s

5 readings

Dot Products10mConstraints for Optimization10mLagrange Approach for Estimation10mModel Interpretability Plots10mAutotuning Options for Support Vector Machines in Model Studio10m

5 practice exercises

Question 5.015mQuestion 5.025mQuestion 5.035mQuestion 5.045mSupport Vector Machines Quiz30m

Week6

Week 6

5 hours to complete

Model Deployment

5 hours to complete

16 videos (Total 36 min), 5 readings, 13 quizzes

16 videos

Introduction to Model Deployment24sEssential Deployment Tasks34sSelecting a Model45sNumeric Measures of Model Performance1mConfusion Matrix for Decision Predictions1mROC Charts and the C-Statistic3mCharts Based on Response Rate: CPH and Lift3mWays of Comparing Models in Model Studio1mDemo: Comparing Models within a Pipeline2mDemo: Comparing Models across Pipelines2mDemo: Reviewing a Project Summary Report on the Insights Tab3mDemo: Registering the Champion Model59sDemo: Exploring the Settings for Model Selection2mScoring and Managing the Champion Model1mDemo: Viewing the Score Code and Running a Scoring Test6mMonitoring and Updating the Model1m

5 readings

Numeric Measures of Model Performance by Prediction Type10mModel Selection Statistics by Target Type10mScore Code and Model Deployment10mIntroducing SAS Model Manager10mSAS Model Manager Tabs10m

6 practice exercises

Question 6.015mQuestion 6.025mQuestion 6.035mQuestion 6.045mQuestion 6.055mModel Deployment Quiz30m

Week7

Week 7

2 hours to complete

Additional Resources and Practice Exam

2 hours to complete

1 video (Total 6 min), 4 readings, 1 quiz

Reviews

4.7

18 reviews

5 stars
75.25%
4 stars
18.55%
3 stars
6.18%

TOP REVIEWS FROM MACHINE LEARNING USING SAS VIYA

by PDJun 11, 2020

A well designed and thoughtful course explaining the key concepts of machine learning

by EOJan 5, 2021

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.

by RMApr 7, 2020

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.

by SSMar 13, 2020

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

View all reviews

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Machine Learning Using SAS Viya

Machine Learning Using SAS Viya

About this Course