Supervised Machine Learning Pipelines Using SAS Viya

Course Code:
SAS-CPML35

Duration:
2 days
8.30am to 5.30pm  
Course Fees:
S$2,800 (excl of G.S.T)
2020 Course Dates
8 – 9 Sep 2020
30 Nov – 1 Dec 2020
None of the published dates will work for you? Speak to our training consultants for a private tuition arrangement or a closed door training.

Course Overview

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.

Program Objectives

• 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.

Course Outline

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

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