Machine Learning Using SAS Viya

1 Videos

• Introduction

1 Assignment

• Question - Model Studio

1 External Tool

• Practice - Creating a Project and Loading Data

5 Videos

• Machine Learning in SAS Viya
• Analytics Life Cycle
• Case Study: Customer Churn
• SAS Viya Tools for Machine Learning
• Demo: Creating a Project and Loading Data

4 Readings

• Applications of Prediction-Based Decision Making
• Case Study: Data Dictionary
• SAS Viya Applications Menu
• SAS Drive

3 Assignment

• Question - Data Partitions
• Question - Missing Values
• Question - Pipeline Templates

2 External Tool

• Practice - Modifying the Data Partition
• Practice - Building a Pipeline from a Basic Template

7 Videos

• Predictive Modeling
• Data Preparation and Preprocessing
• Dividing the Data
• Addressing Rare Events Using Event-Based Sampling
• Demo: Modifying the Data Partition
• Managing Missing Values
• Demo: Building a Pipeline from a Basic Template

9 Readings

• SAS Viya Tools for Data Preparation
• Cross Validation for Small Data Sets
• Selecting Variables on the Data Tab
• Global Metadata
• Managing Missing Values: Details
• Imputation Methods in Model Studio
• Automated Pipeline Creation
• Pipeline Templates in Model Studio
• Logistic Regression

1 Assignment

• Question - Caslibs

4 Readings

• SAS Cloud Analytic Services
• SAS Viya: A Shift in Mindset
• Data Sources and CAS
• Interfaces and Products

1 Assignment

• Getting Started with Machine Learning and SAS Viya Review Quiz

1 Videos

• Introduction to Data Preprocessing and Algorithm Selection

1 Assignment

• Question - Data Exploration Node

2 External Tool

• Practice - Exploring the Data
• Practice - Replacing Incorrect Values Starting on the Data Tab

4 Videos

• Exploring the Data
• Demo: Exploring the Data
• Replacing Incorrect Values
• Demo: Replacing Incorrect Values Starting on the Data Tab

3 Readings

• Data Preprocessing Nodes in Model Studio
• Risk Modeling Add-on for SAS Viya
• Replacing Incorrect Values Starting with the Manage Variables Node

1 Assignment

• Question - Text Mining Node

1 External Tool

• Practice - Adding Text Mining Features

3 Videos

• Feature Extraction
• Text Mining
• Demo: Adding Text Mining Features

2 Readings

• Singular Value Decomposition and the Text Mining Node
• Feature Extraction Node

2 Assignment

• Question - Transformations
• Question 2.04

1 External Tool

• Practice - Transforming Inputs

2 Videos

• Using Transformations to Handle Extreme or Unusual Values
• Demo: Transforming Inputs

1 Readings

• Transformations in Model Studio

1 Assignment

• Question - Variable Selection Node

2 External Tool

• Practice - Selecting Features
• Practice - Saving a Pipeline to the Exchange

3 Videos

• Selecting Useful Inputs
• Demo: Selecting Features
• Demo: Saving a Pipeline to the Exchange

1 Readings

• Feature Selection and the Variable Selection Node: Details

1 Assignment

• Question - Data Collection Challenges

2 Readings

• Best Practices for Common Data Preparation Challenges
• Feature Engineering in SAS Viya

1 Assignment

• Question - Algorithm Selection

1 Videos

• Starting the Discovery Phase of the Analytics Life Cycle

3 Readings

• Supervised Learning Algorithms in Model Studio
• Considerations for Selecting an Algorithm
• Comparison of Modeling Algorithms

1 Assignment

• Data Preprocessing and Algorithm Selection Review Quiz

1 Videos

• Introduction to Decision Trees and Ensembles of Trees

1 Assignment

• Question - Decision Tree Nodes

1 External Tool

• Practice - Building a Decision Tree Using the Default Settings

2 Videos

• Basics of Decision Trees
• Demo: Building a Decision Tree Model Using the Default Settings

3 Readings

• Supervised Learning Node Results Window
• Score Code in Model Studio
• Interactively Edit a Decision Tree

2 Assignment

• Question - Decision Tree Splits
• Question - Decision Trees

1 External Tool

• Practice - Modifying the Structure Parameters

4 Videos

• Decision Trees for Categorical Targets: Classification Trees
• Decision Trees for Interval Targets: Regression Trees
• Improving the Decision Tree Model
• Demo: Modifying the Structure Parameters

3 Assignment

• Question - Leaf Purity
• Question - Number of Splits
• Think about it

1 External Tool

• Practice - Modifying the Recursive Partitioning Parameters

4 Videos

• Recursive Partitioning
• Splitting Criteria
• Split Search
• Demo: Modifying the Recursive Partitioning Parameters

6 Readings

• Impurity Reduction Measures for Categorical and Interval Targets
• Splitting Criteria in Model Studio
• Adjustments in a Split Search
• Missing Values in Decision Trees in Model Studio
• Surrogate Splits
• Calculating Variable Importance for Surrogate Splits

1 Assignment

• Question - Pruning

1 External Tool

• Practice - Modifying the Pruning Parameters

4 Videos

• Optimizing the Complexity of a Decision Tree Model
• Pruning
• Demo: Modifying the Pruning Parameters
• Regularizing and Tuning the Hyperparameters of a Machine Learning Model

5 Readings

• Bottom-Up Pruning Requirements
• Pruning Options in Model Studio
• Hyperparameter Optimization Approaches
• Autotuning Options for Decision Trees in Model Studio
• Use the Best Hyperparameter Values for a Model

3 Assignment

• Question - Perturb and Combine Methods
• Question Tree-Based Models
• Question - Gradient Boosting versus Forest Models

2 External Tool

• Practice - Building a Gradient Boosting Model
• Practice - Building a Forest Model

8 Videos

• Building Ensemble Models
• Perturb and Combine Methods
• Bagging
• Boosting
• Comparison of Tree-Based Models
• Demo: Building a Gradient Boosting Model
• Forest Models
• Demo: Building a Forest Model

3 Readings

• Gradient Boosting Models
• Autotuning Options for Gradient Boosting in Model Studio
• Autotuning Options for Forests in Model Studio

1 Assignment

• Decision Trees and Ensembles of Trees Review Quiz

1 Videos

• Introduction to Neural Networks

3 Assignment

• Question - Universal Approximators
• Question - Parameter and Intercept Estimates
• Question - Optimization Methods

1 External Tool

• Practice - Building a Neural Network Using the Default Settings

7 Videos

• Beyond Traditional Regression: Neural Networks
• Overcoming the Limitations of Neural Networks
• Basics of Neural Networks
• Estimating Weights and Making Predictions
• Learning Process
• Essential Discovery Tasks for Neural Networks
• Demo: Building a Neural Network Using the Default Settings

4 Readings

• Standardization Methods
• Iterative Updating in Numerical Optimization
• Numerical Optimization Methods in Model Studio
• Deviance Measures in Model Studio

1 Assignment

• Question - Deep Learning Models

1 External Tool

• Practice - Modifying the Neural Network Architecture

5 Videos

• Improving the Neural Network Model
• Neural Network Architectures
• Activation Functions
• Shaping the Sigmoid
• Demo: Modifying the Neural Network Architecture

5 Readings

• Stationary Versus Nonstationary Data
• Calculating the Number of Parameters
• Deep Learning
• Hidden Layer Activation Functions in Model Studio
• Target Layer Activation Functions and Error Functions in Model Studio

1 Assignment

• Question - Model Generalization and Early Stopping

1 External Tool

• Practice - Modifying the Learning and Optimization Parameters

6 Videos

• Optimizing the Complexity of a Neural Network Model
• Weight Decay
• Early Stopping
• Regularizing and Tuning the Hyperparameters of a Neural Network Model
• Network Learning Hyperparameters
• Demo: Modifying the Learning and Optimization Parameters

2 Readings

• Important Hyperparameters for Neural Networks: Summary
• Autotuning Options for Neural Networks in Model Studio

1 Assignment

• Neural Networks Review Quiz

1 Videos

• Introduction to Support Vector Machines

1 Assignment

• Question - SVM and the Curse of Dimensionality

1 External Tool

• Practice - Building a Support Vector Machine Using the Default Settings

5 Videos

• Support Vector Machines as Classifier Models
• Mathematical Definition of a Support Vector Machine
• Maximum-Margin Hyperplane and Support Vectors
• Essential Discovery Tasks for Support Vector Machines
• Demo: Building a Support Vector Machine Using the Default Settings

1 Readings

• Dot Products

1 Assignment

• Question - Non-linearly Separable Data

1 External Tool

• Practice - Modifying the Methods of Solution Parameters

4 Videos

• Improving the Support Vector Machine Model
• Optimization Problem
• Accounting for Errors with Nonlinearly Separable Data
• Demo: Modifying the Methods of Solution Parameters

3 Readings

• Constraints for Optimization
• Number of Support Vectors
• Lagrange Approach for Estimation

2 Assignment

• Question - Kernel Functions
• Question - Model Interpretability

2 External Tool

• Practice - Increasing the Flexibility of the Support Vector Machine
• Practice - Adding Model Interpretability

7 Videos

• Optimizing the Complexity of the Support Vector Machine Model
• Feature Space Approach for Nonlinearly Separable Data
• Kernel Trick
• Demo: Increasing the Flexibility of the Support Vector Machine
• Model Interpretability
• Demo: Adding Model Interpretability
• Regularizing and Tuning the Hyperparameters of the Support Vector Machine Model

3 Readings

• SVM Properties Related to Kernels and Training Algorithms
• Model Interpretability Plots
• Autotuning Options for Support Vector Machines in Model Studio

1 Assignment

• Support Vector Machines Review Quiz

1 Videos

• Introduction to Model Deployment

4 Assignment

• Question - C-Statistic
• Question - Model Comparison Node
• Question - Model Selection Statistics
• Question - Model Comparison Node Properties

5 External Tool

• Practice - Comparing Models Within a Pipeline
• Practice - Comparing Models Across Pipelines
• Practice - Reviewing a Project Summary Report on the Insights Tab
• Practice - Registering the Champion Model
• Practice - Exploring the Settings for Model Selection

12 Videos

• Essential Deployment Tasks
• Selecting a Model
• Numeric Measures of Model Performance
• Confusion Matrix for Decision Predictions
• ROC Charts and the C-Statistic
• Charts Based on Response Rate: CPH and Lift
• Ways of Comparing Models in Model Studio
• Demo: Comparing Models within a Pipeline
• Demo: Comparing Models across Pipelines
• Demo: Reviewing a Project Summary Report on the Insights Tab
• Demo: Registering the Champion Model
• Demo: Exploring the Settings for Model Selection

3 Readings

• Numeric Measures of Model Performance by Prediction Type
• Score Holdout Data (Out-of-Time Testing)
• Model Selection Statistics by Target Type

1 Assignment

• Question - Scoring

1 External Tool

• Practice - Viewing the Score Code and Running a Scoring Test

4 Videos

• Scoring and Managing the Champion Model
• Demo: Viewing the Score Code and Running a Scoring Test
• Assessing Model Bias
• Monitoring and Updating the Model

5 Readings

• Score Code and Model Deployment
• Internally Scored Data Sets in Model Studio
• Introducing SAS Model Manager
• SAS Model Manager Tabs
• Assessing Model Bias and Relevant Resources

1 Assignment

• Model Assessment and Deployment Review Quiz

1 Videos

• Introduction to Additional Nodes

1 External Tool

• Practice - Adding Open Source Models to a Model Studio Project

1 Videos

• Demo: Adding Open Source Models to a Model Studio Project

4 Readings

• Additional Nodes in Model Studio
• Save Data Node
• SAS Code Node
• Open Source Code Node

1 External Tool

• Certification Practice Exam: Machine Learning Specialist