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Machine Learning and Predictive Models

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Machine Learning and Predictive Models


This training course includes interactive discussions and utilizes a variety of exercises and case studies. Each machine learning algorithm is supported by a dedicated case study, with detailed outputs aligned with multi-stage analysis. All algorithms are elaborated with sequential image applications within comparative techniques 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.
  • Grasp the fundamental differences between data analysis and machine learning.
  • Apply testing and validation of samples in machine learning models.
  • Provide a general perspective on best software solutions in analysis.
  • Apply accurate estimations with complete predictive models.

Target Audience

This training course targets all professionals who are interested in gaining further knowledge about the practical concepts of machine learning that they can leverage within their organizations. This includes professionals working in various fields, including but not limited to banking, insurance, retail, government, manufacturing, healthcare, telecommunications, and airline companies.

Program Content

  • Data Analysis and Simple Regression Introduction to Data Analysis Logic Regression and Proportional Two-Sample Testing Representation of Two Samples in a Single Plot Multiple Group Testing Based on Regression and Proportion Representation of Multiple Groups in a Single Plot Simple Regression Regression versus Correlation Sensitivity Analysis of Quantitative Variables
  • Multiple and Logistic Regression Introduction to Machine Learning Gradual Regression Logic Multiple Regression versus Simple Regression Variable Analysis for Estimations Dummy Variables Similarities and Differences between Logistic and Multiple Regression Simplifying Complex Models Stepwise Regression
  • Discriminant Analysis Optimal Pattern Discriminant Analysis Based on Two Groups Case Assignment Model Evaluation Classification Functions Mahalanobis Squared Distances Probability Methodology Model Reduction Generalized Discriminant Analysis
  • Decision Trees What is a Decision Tree? Binary Trees Decision Tree Quality Data Pruning Rules CART: Classification Tree CART: Regression Tree CHAID Tree Random Forest Tree
  • Nearest Neighbor, Bayesian Inference, Neural Network, Deep Learning Conditional Probabilities Prediction through Probabilities Distance between Proximal Values K Nearest Neighbor Function for Proximal Values Weights in Neural Network Models Role of Hidden Layers Pros and Cons of Neural Networks Deep Learning (Artificial Intelligence) Introduction to Big Data

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