Course Summary
Learn how to apply machine learning algorithms using SAS software. This course covers fundamental concepts of machine learning and provides hands-on experience with SAS software.Key Learning Points
- Gain practical experience with SAS software for machine learning
- Understand fundamental concepts of machine learning
- Apply machine learning algorithms to real-world problems
Related Topics for further study
Learning Outcomes
- Ability to apply machine learning algorithms using SAS software
- Understanding of fundamental concepts of machine learning
- Practical experience with real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics
- Familiarity with SAS software
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Hands-on
Similar Courses
- Machine Learning with Python
- Applied Data Science with Python
Related Education Paths
Related Books
Description
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.
Outline
- Course Overview
- Welcome to the Course!
- Using SAS® Viya® for Learners with This Course (Required)
- Using Forums and Getting Help
- Getting Started with Machine Learning using SAS® Viya®
- Introduction
- Machine Learning in SAS Viya
- Analytics Life Cycle
- Case Study: Customer Churn
- SAS Viya Tools for SAS Visual Data Mining and Machine Learning
- Demo: Creating a Project and Selecting Data
- Importing Data from a Local Source
- Predictive Modeling
- Data Preparation
- 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
- SAS Viya in the SAS Platform: Architecture
- Applications of Prediction-Based Decision Making
- Case Study: Data Dictionary
- SAS Drive and the Application Bar
- Importing Data from a Local Source
- 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
- Pipeline Templates in Model Studio
- Logistic Regression
- SAS Cloud Analytic Services
- SAS Viya: A Shift in Mindset
- Data Sources and CAS
- Interfaces and Products
- Question 1.02
- Question 1.03
- Question 1.04
- Question 1.05
- Question 1.06
- Getting Started with Machine Learning and SAS Viya
- Data Preparation and Algorithm Selection
- Introduction to Data Preparation and Algorithm Selection
- Exploring the Data
- Demo: Exploring the Data
- Replacing Incorrect Values
- Demo: Replacing Incorrect Values Starting on the Data Tab
- Feature Creation
- 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
- Data Mining Preprocessing Nodes in Model Studio
- Risk Modeling Add-on for SAS Visual Data Mining and Machine Learning
- Replacing Incorrect Values Starting with the Manage Variables Node
- Singular Value Decomposition and the Text Mining Node
- Feature Extraction Node
- Transformations in Model Studio
- Feature Selection and the Variable Selection Node: Details
- Variable Clustering
- Best Practices for Common Data Preparation Challenges
- Feature Engineering in SAS Visual Data Mining and Machine Learning
- Considerations for Selecting an Algorithm
- Comparison of Modeling Algorithms
- Question 2.01
- Question 2.02
- Question 2.03
- Question 2.04
- Question 2.05
- Question 2.07
- Question 2.08
- Data Preparation and Algorithm Selection Quiz
- Decision Trees and Ensembles of Trees
- 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
- Score Code in Model Studio
- 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
- Bottom-Up Pruning Requirements
- Pruning Options in Model Studio
- Hyperparameter Optimization Methods
- Autotuning Options for Decision Trees in Model Studio
- Gradient Boosting Models
- Autotuning Options for Gradient Boosting in Model Studio
- Autotuning Options for Forests in Model Studio
- Question 3.01
- Question 3.02
- Question 3.03
- Question 3.04
- Question 3.05
- Question 3.06
- Question 3.07
- Question 3.08
- Question 3.09
- Decision Trees and Ensembles of Trees Quiz
- Neural Networks
- 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
- Standardization Methods
- Iterative Updating in Numerical Optimization
- Numerical Optimization Methods in Model Studio
- Deviance Measures in Model Studio
- Calculating the Number of Parameters
- Deep Learning
- Hidden Layer Activation Functions in Model Studio
- Target Layer Activation Functions and Error Functions in Model Studio
- Autotuning Options for Neural Networks in Model Studio
- Question 4.01
- Question 4.02 - 4.03
- Question 4.04
- Question 4.05
- Question 4.06 - 4.07
- Neural Networks Quiz
- Support Vector Machines
- 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
- Dot Products
- Constraints for Optimization
- Lagrange Approach for Estimation
- Model Interpretability Plots
- Autotuning Options for Support Vector Machines in Model Studio
- Question 5.01
- Question 5.02
- Question 5.03
- Question 5.04
- Support Vector Machines Quiz
- Model Deployment
- 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
- Monitoring and Updating the Model
- Numeric Measures of Model Performance by Prediction Type
- Model Selection Statistics by Target Type
- Score Code and Model Deployment
- Introducing SAS Model Manager
- SAS Model Manager Tabs
- Question 6.01
- Question 6.02
- Question 6.03
- Question 6.04
- Question 6.05
- Model Deployment Quiz
- Additional Resources and Practice Exam
- Demo: Adding Open Source Models to a Model Studio Project
- Additional Nodes in Model Studio
- Save Data Node
- SAS Code Node
- Open Source Code Node
Summary of User Reviews
Discover the world of machine learning with SAS in this comprehensive course. Students rave about the practical application of the material and the engaging teaching style.Key Aspect Users Liked About This Course
Practical application of the materialPros from User Reviews
- The course covers a wide range of topics and provides practical examples for each one.
- The teaching style is engaging and easy to follow.
- The instructors are knowledgeable and responsive to questions.
- The course includes hands-on exercises that reinforce the concepts taught.
- The course is well-structured and easy to navigate.
Cons from User Reviews
- Some students found the course content to be too basic.
- The course does not cover some advanced machine learning techniques.
- The course can be time-consuming, especially if you want to complete all the exercises.
- The course is not free, which may be a barrier for some students.
- Some students had technical difficulties with the online platform.
Jeff Thompson
4.7 Raiting
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