SAS Tutorial | Machine Learning in SAS for Regression and Classification

The most popular buzz word nowadays in technology world is “machine learning.” Machine learning is the computer science technology that provides systems with the ability to learn without being explicitly programmed. A lot of organizations and start-up companies take advantage of machine learning to solve business problems. Right now, machine learning can help us with the following: Self-driving vehicles. Online recommendations in Netflix and Amazon. Fraud detection in banks. Image and video recognition. Security monitoring. Natural language processing. Question-answering machines (e.g., IBM Watson). Machine learning (ML) is expected to automate and optimize many processes, providing tremendous value to organizations and societies, so a lot of companies are also looking to implement ML in their organizations. The tutorial is intended for SAS programmers who are interested in learning about machine learning and applying ML to lead innovation in their organizations. Through this tutorial, SAS® programmers will be able to achieve the following: Understanding of machine learning. Knowledge on concepts and theory of machine learning. Potential and impact of machine learning. Interest, excitement and opportunities for SAS programmers. Current, future and practical implementation of machine learning. Confidence in machine learning.

Content Outline
0:00:00 – Welcome
0:10:18 – Principal Components Analysis, Principal Components Regression, and Partial Least Squares
0:38:53 – Ridge Regression, LASSO, and Elastic Net
1:03:39 – Generalized Additive Models
1:20:12 – Decision Trees, Part 1
1:43:56 – Decision Trees, Part 2
2:10:40 – Random Forests, Support Vector Machines, and Conclusions

Session Id: 5307
Presenter: Kevin Lee, Clindata Insight Inc.
Topic: Analytics
Industry: Non-Industry Specific
Audience: Programmer, Statistician/Biostatistician
Level: Novice

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