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This course will provide an overview of a many of additional machine learning concepts, techniques, and algorithms, ranging from basic classification to decision trees and clustering. You will learn how to apply, test, and interpret machine learning algorithms as alternative methods for answering research questions by completing this course.
Syllabus :
1. Decision Trees
- What Is Machine Learning?
- Machine Learning and the Bias Variance Trade-Off
- What Is a Decision Tree?
- What is the Process of Growing a Decision Tree?
- Building a Decision Tree with SAS
- Strengths and Weaknesses of Decision Trees in SAS
- Building a Decision Tree with Python
2. Random Forests
- What Is A Random Forest and How Is It "Grown"?
- Building a Random Forest with SAS
- Building a Random Forest with Python
- Validation and Cross-Validation
3. Lasso Regression
- What is Lasso Regression?
- Testing a Lasso Regression with SAS
- Data Management for Lasso Regression in Python
- Testing a Lasso Regression Model in Python
- Lasso Regression Limitations
4. K-Means Cluster Analysis
- What Is a k-Means Cluster Analysis?
- Running a k-Means Cluster Analysis in SA
- Running a k-Means Cluster Analysis in Python
- k-Means Cluster Analysis Limitations
