Machine Learning and Predictive Models

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.

Rome - ItalyData Management and Business Intelligence

Course schedules

Upcoming dates Classroom

Start date	End date	Location	Fee	Registration
5 Oct 2026	9 Oct 2026	Rome - Italy	€2,125 + VAT	→
16 Nov 2026	20 Nov 2026	Munich - Germany	€1,725 + VAT	→
7 Dec 2026	11 Dec 2026	Amsterdam - Netherlands	€2,125 + VAT	→
11 Jan 2027	15 Jan 2027	Cape Town - South Africa	€1,420 + VAT	→
8 Feb 2027	12 Feb 2027	Frankfurt - Germany	€1,625 + VAT	→
8 Mar 2027	12 Mar 2027	Barcelona - Spain	€1,925 + VAT	→
12 Apr 2027	16 Apr 2027	Barcelona - Spain	€1,925 + VAT	→
10 May 2027	14 May 2027	Rome - Italy	€2,125 + VAT	→

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Online live Remote

5 - 9 October 2026 €1,050 + VAT → 16 - 20 November 2026 €1,050 + VAT → 7 - 11 December 2026 €1,050 + VAT → 11 - 15 January 2027 €1,050 + VAT → 8 - 12 February 2027 €1,050 + VAT → 8 - 12 March 2027 €1,050 + VAT → 12 - 16 April 2027 €1,050 + VAT → 10 - 14 May 2027 €1,050 + VAT →

Why Attend

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 Objectives

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

Designed for

This program is intended for professionals involved in Data Management and Business Intelligence.

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.

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 content

Day1

Data Analysis and Simple Regression

Fundamentals of Data Analysis Logic
Comparing two groups: Means and proportions testing
Visualizing group profiles in a single chart
Analyzing multiple groups: Means and proportions testing
Profiling multiple groups in one chart
Introduction to Simple Regression
Regression vs. Correlation
Sensitivity analysis for quantitative variables

Day2

Multiple and Logistic Regressions

Overview of Machine Learning principles
Understanding Gradient Descent logic
Differences between Multiple and Simple Regression
Variability analysis in estimations
Utilizing dummy variables in models
Key distinctions between Logistic and Multiple Regressions
Simplifying complex models through Stepwise Regression

Day3

Discriminant Analysis

Optimized profiling techniques
Two-Group Discriminant Function Analysis
Case attribution and model evaluation
Classification functions and Mahalanobis squared distances
Probability-based methods and model reduction
Generalized Discriminant Analysis

Day4

Decision Trees

Introduction to Decision Trees
Binary Trees and their quality assessment
Rules and techniques for pruning
CART Models: Classification and Regression Trees
CHAID Trees and Random Forest Trees

Day5

Nearest Neighbor, Bayesian, Neural Network and Deep Learning

Understanding conditional probabilities for prediction
Prediction using probability models
Distance-based predictions (Nearest Neighbor)
K-Nearest Neighbors methodology
Neural Network models: Weights, hidden layers, pros, and cons
Introduction to Deep Learning concepts
Overview of Big Data principles

The certificate

SPARK Training Certificate of Completion is awarded to participants who attend and complete the full training course.

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