Specialized Training Center | Machine Learning and Predictive Models

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Introduction

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, Statistics and SPSS), enabling participants to become expert practitioners by evaluating and selecting appropriate solutions with suitable technical packages for their organizations.

Goals

Participants will learn:

Predictive Analysis.
Predictive Models.
Data Analysis.
Data Analytic Models.

TRAINING OBJECTIVES

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.

Training 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, Statistics and Excel.

Target Groups

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.

Program Content

Data Analysis and Simple Regression

Introduction to Data Analysis Logic.
Testing two groups on their means and proportions.
Profiling two groups in one single chart.
Testing multiple groups on their means and proportions.
Profiling multiple groups in one single chart.
Simple regression.
Regression vs. Correlation.
Sensitivity analysis of quantitative variables.

Multiple and Logistic Regressions

Introduction to Machine Learning.
The Gradient Descent logic.
Multiple Regression vs. Simple Regression.
Variability analysis for estimations.
Dummy variables.
Similarities and differences between Logistic and Multiple regressions.
Simplifying complex models.
Stepwise regression.

Discriminant Analysis

Optimized Profiling.
Two-Group Discriminant Function.
Attribution of Cases.
Model Evaluation.
Classification Functions.
Mahala Nobis Squared Distances.
Probability Method.
Model’s Reduction.
Generalized Discriminant Analysis.

Decision Trees

What are Decision Trees?
Binary Trees.
Quality of a Decision Tree.
Rules of pruning.
CART: Classification Tree.
CART: Regression Tree.
CHAID Tree.
Random Forest Tree.

Nearest Neighbor, Bayesian, Neural Network and Deep Learning

Conditional probabilities.
Prediction by probabilities.
Distance from neighbors.
K nearest distances from neighbors.
Weights in a Neural Network model.
Hidden layers role.
Neural Network pros and cons.
Deep Learning.
Introduction to Big Data.

Code	Date of meeting	Place	Course Price
DIT10	4 - 8 August 2024	Online training	$3,900	Register
DIT10	4 - 8 August 2024	Dubai-Emirates	$3,900	Register
DMBI47	22 - 26 September 2024	Online training	$3,900	Register
DMBI47	22 - 26 September 2024	Dubai-Emirates	$3,900	Register
DIT20	13 - 17 October 2024	Online training	$3,900	Register
DIT20	13 - 17 October 2024	Dubai-Emirates	$3,900	Register
DIT30	22 - 26 December 2024	Online training	$3,900	Register
DIT30	22 - 26 December 2024	Dubai-Emirates	$3,900	Register

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Machine Learning and Predictive Models

Introduction

Goals

Training Methodology

Target Groups

Program Content

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