- Guaranteed To Run
- All Courses
- ALL MICROSOFT COURSES
- FUNDED COURSES
- Avantus Learning Offers
- About Us
- Rewards Account Login
This course combines data exploration, visualization, data preparation, feature engineering, sampling and partitioning, model training, scoring, and assessment. It covers a variety of statistical, data mining, and machine learning techniques performed in a scalable and in-memory execution environment. The course provides theoretical foundation and hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The course includes predictive modeling techniques such as linear and logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, and factorization machine.
Business analysts, data analysts, marketing analysts, marketing managers, data scientists,
data engineers, financial analysts, data miners, statisticians, mathematicians, and others who
work in correlated areas
• Apply the analytical life cycle to business need.
• Incorporate a business-problem-solving approach in daily activities.
• Prepare and explore data for analytical model development.
• Create and select features for predictive modeling.
• Develop a series of supervised learning models based on different techniques such as decision tree, ensemble of trees (forest and gradient boosting), neural networks, and support vector machines.
• Evaluate and select the best model based on business needs.
• Deploy and manage analytical models under production.
Module 1: Introduction
Module 2: Data Preparation
Module 3: Decision Trees and Ensembles of Trees
Module 4: Neural Network
Module 5: Support Vector Machines and Additional Topics
Module 6: Model Assessment and Deployment