Global Learning for Operational Leaders
Professional training course
Machine Learning and Predictive Models
With advancements in technology, predictive models are now accessible to a wide range of users. The outline covers Data Analysis and Simple Regression, Multiple and Logistic Regressions, Discriminant Analysis, and Decisi...
- 5 daysClassroom or online
- 14Upcoming dates
- 7Cities
- €1,625Fees from
Classroom
14 sessions
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Online / Live
14 sessions
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Introduction
Course overview
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.
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.
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.
Complete course outline
Five focused days of learning and application
The timings provided are estimates and may vary depending on the number of participants and the discussions taking place during the sessions. They are indicative only and do not constitute a binding commitment.
Day 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
- Coffee break
- Analyzing multiple groups: Means and proportions testing (continued)
- Profiling multiple groups in one chart
- Introduction to Simple Regression
- Regression vs. Correlation
- Sensitivity analysis for quantitative variables
Day 2
Multiple and Logistic Regressions
- Overview of Machine Learning principles
- Understanding Gradient Descent logic
- Differences between Multiple and Simple Regression
- Variability analysis in estimations
- Coffee break
- Variability analysis in estimations (continued)
- Utilizing dummy variables in models
- Key distinctions between Logistic and Multiple Regressions
- Simplifying complex models through Stepwise Regression
Day 3
Discriminant Analysis
- Optimized profiling techniques
- Two-Group Discriminant Function Analysis
- Case attribution and model evaluation
- Coffee break
- Case attribution and model evaluation (continued)
- Classification functions and Mahalanobis squared distances
- Probability-based methods and model reduction
- Generalized Discriminant Analysis
Day 4
Decision Trees
- Introduction to Decision Trees
- Binary Trees and their quality assessment
- Rules and techniques for pruning
- Coffee break
- Rules and techniques for pruning (continued)
- CART Models: Classification and Regression Trees
- CHAID Trees and Random Forest Trees
Day 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
- Coffee break
- K-Nearest Neighbors methodology (continued)
- Neural Network models: Weights, hidden layers, pros, and cons
- Introduction to Deep Learning concepts
- Overview of Big Data principles
Course FAQs
Questions about this course
What participants and training managers usually ask before booking Machine Learning and Predictive Models. Can't find your answer?
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 16 - 20 November 2026. See the course schedules section for the available dates and delivery formats.
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.
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?
Check the course schedules section for currently published classroom and Online / Live sessions.
Where are classroom sessions delivered?
Current classroom venues include Munich, Amsterdam, Cape Town, Frankfurt, Barcelona, Rome, Paris.
How long is the training programme?
The programme runs for five days from 09:00 to 14:00, which gives 25 contact hours, including a daily 30-minute break (22.5 hours of instruction).
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