Course: Machine Learning Using SAS Viya
Modules:
Welcome to the Course!
Introduction, 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, 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
Introduction to Data Preprocessing and Algorithm Selection, Exploring the Data, Demo: Exploring the Data, Replacing Incorrect Values, Demo: Replacing Incorrect Values Starting on the Data Tab, Feature Extraction, Text Mining, Demo: Adding Text Mining Features, Using Transformations to Handle Extreme or Unusual Values, Demo: Transforming Inputs, Selecting Useful Inputs, Demo: Selecting Features, Demo: Saving a Pipeline to the Exchange, Starting the Discovery Phase of the Analytics Life Cycle
Introduction to Decision Trees and Ensembles of Trees, Basics of Decision Trees, Demo: Building a Decision Tree Model Using the Default Settings, Decision Trees for Categorical Targets: Classification Trees, Decision Trees for Interval Targets: Regression Trees, Improving the Decision Tree Model, Demo: Modifying the Structure Parameters, Recursive Partitioning, Splitting Criteria, Split Search, Demo: Modifying the Recursive Partitioning Parameters, Optimizing the Complexity of a Decision Tree Model, Pruning, Demo: Modifying the Pruning Parameters, Regularizing and Tuning the Hyperparameters of a Machine Learning Model, 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
Introduction to Neural Networks, 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, Improving the Neural Network Model, Neural Network Architectures, Activation Functions, Shaping the Sigmoid, Demo: Modifying the Neural Network Architecture, 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
Introduction to Support Vector Machines, 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, Improving the Support Vector Machine Model, Optimization Problem, Accounting for Errors with Nonlinearly Separable Data, Demo: Modifying the Methods of Solution Parameters, 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
Introduction to Model Deployment, 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, Scoring and Managing the Champion Model, Demo: Viewing the Score Code and Running a Scoring Test, Assessing Model Bias, Monitoring and Updating the Model
Introduction to Additional Nodes, Demo: Adding Open Source Models to a Model Studio Project
Certification Practice Exam: Machine Learning Specialist