Machine Learning and Predictive Models

Why Attend

Predictive models have become accessible to all users with the advancement of technology. This course offers a complete overview of supervised Machine Learning algorithms, and their role in the enhancement of predictions in most industries and by most organizations.

This course covers all models utilized under different technologies (SAS, Statistica and SPSS), enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations.

Course Methodology

This course includes interactive discussion and the use of exercises and case studies.  Each Machine Learning algorithm is supported by its own case study with step by step outputs that go in parallel with its multi stage analysis. All algorithms are detailed with sequential screen shot applications on comparative technologies such as SPSS, SAS, Statistica and Excel.

Course Objectives

By the end of the course, participants will be able to:

• Understand the true meaning of Machine Learning
• Comprehend the key differences between Data Analysis and Machine Learning
• Apply testing and validating samples into Machine Learning models
• Submit an overview of the best analytic solutions
• Implement fine tuned estimation with complete predictive models 

Target Audience

Any level of professional interested in how Machine Learning can assist their organization, would benefit from this course.  These include professionals from industries including, but not limited to, banking, insurance, retail, government, manufacturing, healthcare, telecom, and airlines.

Target Competencies

• Predictive Analysis
• Predictive Models
• Data Analysis
• Data Analytic Models