Why Attend
With advancements in technology, predictive models are now accessible to a wide range of users. This course provides a comprehensive overview of supervised Machine Learning algorithms and their critical role in enhancing predictions across industries and organizations.
Participants will explore various models across different technologies, including SAS, Statistica, and SPSS. By the end of the course, they will be equipped to evaluate and select the most suitable solutions and technical packages tailored to their organization's needs, becoming expert practitioners in the field.
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:
- Gain a clear understanding of Machine Learning concepts
- Differentiate between Data Analysis and Machine Learning methodologies
- Apply testing and validation techniques to Machine Learning models
- Present an overview of optimal analytic solutions
- Build and fine-tune predictive models for accurate estimations
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
