Global Learning for Operational Leaders
Classroom
16 sessions
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Online / Live
16 sessions
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Introduction
Course overview
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
What you will achieve
Learning 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
Who should attend
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
Methodology
Learning approach
- 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
Course outline and key learning areas
Module 1
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
Module 2
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
Module 3
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
Module 4
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
Module 5
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
FAQ
Frequently asked questions
What does Machine Learning and Predictive Models cover?
This course covers Data Management and Business Intelligence through a structured five-day outline focused on practical application, discussion, and implementation planning.
When is the next available session?
The next scheduled session starts on 14 - 18 September 2026, with additional classroom dates and mirrored Online / Live options listed in the course schedules section.
Who should attend this course?
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
Is bilingual course delivery available?
Yes. All course materials and presentations are provided in English, while bilingual explanation and discussion support may be available depending on the selected location and trainer availability. For example, when attending a course in Istanbul, you may request a Turkish-speaking trainer to explain the English course content in Turkish. Arabic explanation support may also be requested at this location. Other languages are available in selected cities. You can choose your preferred language support as an optional preference during registration.
How can I register for a session?
Use any Register button next to the available course dates to open the participant registration page and submit your booking request for the selected session.
Is this course available online as well as classroom-based?
Yes. The course detail page includes both classroom sessions and Online / Live sessions, with online options aligned to the same course dates for easier planning.
Where are classroom sessions delivered?
Current classroom venues include Barcelona, Rome, Munich, Amsterdam, Paris, Frankfurt.
How long is the training programme?
The programme runs for five days, with up to five training hours per day, from 9:00 AM to 2:00 PM. Each programme includes a total of 25 accredited training hours.
Still Have Questions?
Contact the academy team for course details, delivery options, and delegate guidance.
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