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DB-017

Machine Learning and Predictive Models

Intensive 5-day course · English, Arabic & European languages

Data analysts and business professionals use this detailed five-day course to learn supervised machine learning algorithms. The syllabus explains how to build, test, and validate predictive models using industry-standard tools to improve statistical estimations across their organisation.

Classroom

Public courses in our cities

  • 19–23 Oct 2026Muscat£3,080 + VAT
  • 2–6 Nov 2026Manama£3,080 + VAT
  • 16–20 Nov 2026Munich£3,400 + VAT
  • 7–11 Dec 2026Amsterdam£3,400 + VAT
  • 21–25 Dec 2026Dubai£4,200 + VAT
  • 11–15 Jan 2027Barcelona£3,080 + VAT
Show 23 more dates
  • 25–29 Jan 2027Geneva£4,200 + VAT
  • 8–12 Feb 2027Barcelona£3,080 + VAT
  • 22–26 Feb 2027Abu Dhabi£4,200 + VAT
  • 1–5 Mar 2027Rome£3,400 + VAT
  • 15–19 Mar 2027Cairo£2,280 + VAT
  • 5–9 Apr 2027Jeddah£4,200 + VAT
  • 19–23 Apr 2027Munich£3,400 + VAT
  • 3–7 May 2027Amsterdam£3,400 + VAT
  • 17–21 May 2027Singapore£4,200 + VAT
  • 14–18 Jun 2027Barcelona£3,080 + VAT
  • 28 Jun – 2 Jul 2027Muscat£3,080 + VAT
  • 12–16 Jul 2027Barcelona£3,080 + VAT
  • 26–30 Jul 2027Casablanca£2,280 + VAT
  • 2–6 Aug 2027Rome£3,400 + VAT
  • 16–20 Aug 2027Manama£3,080 + VAT
  • 6–10 Sep 2027Amman£3,400 + VAT
  • 20–24 Sep 2027Munich£3,400 + VAT
  • 4–8 Oct 2027Amsterdam£3,400 + VAT
  • 18–22 Oct 2027Madrid£3,400 + VAT
  • 8–12 Nov 2027Barcelona£3,080 + VAT
  • 22–26 Nov 2027Berlin£3,400 + VAT
  • 13–17 Dec 2027Barcelona£3,080 + VAT
  • 27–31 Dec 2027Milan£3,400 + VAT

Group discount: 2 delegates 20% · 3 delegates 35% · 5 or more delegates 50%

Can't make these dates? We can run this course for your team on dates that suit you. Ask for custom dates · In-house options

Online (live)

With a trainer, on dates to suit your team

This course can be taught live online by a trainer, in English, Arabic or any European language, for one person or a whole team.

£1,850 + VAT

Group discount: 2 delegates 20% · 3 delegates 35% · 5 or more delegates 50%

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Machine Learning and Predictive Models

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Course overview

Predictive models allow organisations to forecast trends and make better decisions based on historical data. This course covers the supervised machine learning algorithms needed to build accurate predictions. It shows delegates how to apply them across different industries to generate value.

The five days move logically from simple data analysis logic through to multiple regression, decision trees, and detailed neural networks, ensuring delegates learn the differences between logistic regression and discriminant analysis. Practical sessions use step-by-step outputs and detailed case studies for each algorithm.

After completing the training, delegates can evaluate different machine learning solutions, allowing them to choose the best technical package for their specific needs. They leave able to apply validation techniques and fine-tune predictive models effectively using SAS, Statistica, or SPSS.

Course objectives

By the end of the course, delegates will be able to:

  1. Explain core machine learning concepts and terminology
  2. Differentiate between data analysis and machine learning methods
  3. Apply testing and validation techniques to new models
  4. Evaluate and present optimal analytic solutions to stakeholders
  5. Build and fine-tune predictive models for accurate estimations

Target competencies

  • Predictive Analysis
  • Predictive Models
  • Data Analysis
  • Data Analytic Models

Who should attend

This course suits:

  • Professionals at any level interested in organisational uses of machine learning
  • Professionals in banking, insurance and retail
  • Professionals in government, manufacturing and healthcare
  • Professionals in telecommunications and airlines

Training methodology

  • Interactive discussions and exercises accompany sequential software demonstrations.
  • Case studies using SAS, SPSS, Statistica, and Excel
  • Step-by-step multi-stage algorithm analysis

Daily agenda

  1. Day 1

    Data analysis and simple regression

    • Fundamentals of structural data analysis logic
    • Means and proportions testing for two groups
    • Visualising group profiles in a single chart
    • Testing means and proportions across multiple groups
    • Displaying multiple group profiles together in one chart
    • Introduction to simple regression principles
    • Understanding regression versus statistical correlation
    • Sensitivity analysis for quantitative variables
  2. Day 2

    Multiple and logistic regressions

    • Overview of core machine learning principles
    • Understanding gradient descent logic in algorithms
    • Differences between multiple and simple regression
    • Analysing variability in statistical estimations
    • Utilising dummy variables in predictive models
    • Distinctions between logistic and multiple regressions
    • Simplifying detailed models through stepwise regression
  3. Day 3

    Discriminant analysis techniques

    • Applying optimised profiling techniques to data
    • Two-group discriminant function analysis methods
    • Case attribution and overall model evaluation
    • Classification functions and Mahalanobis squared distances
    • Probability-based methods and reduction of model complexity
    • Executing generalised discriminant analysis tasks
  4. Day 4

    Working with decision trees

    • Introduction to the structure of decision trees
    • Binary trees and assessing their overall quality
    • Rules and techniques for effective tree pruning
    • CART models: classification and regression trees
    • Using CHAID trees and random forest algorithms
  5. Day 5

    Nearest neighbour, Bayesian, neural network and deep learning models

    • Understanding conditional probabilities for accurate prediction
    • Generating predictions using probability models
    • Distance-based predictions using the nearest neighbour
    • Executing k-nearest neighbours methodology
    • Neural network weights, hidden layers, pros, and cons
    • Introduction to deep learning and big data principles

Certificate

Every delegate who completes the course receives an ARUK Training Certificate of Attendance.

Frequently asked questions

How do I register?

Choose a date and city in the schedule and press Register, or send us an enquiry. Our team confirms your place and sends an invoice.

Are fees shown with VAT?

Fees are shown per delegate and exclude VAT, which is added to the invoice where applicable.

Can this course be delivered for our team only?

Yes. Any ARUK course can be delivered in-house at your premises, in a meeting room we book for you, or 1-to-1.

Is this course available online?

Yes. We run it live online with a trainer, on dates that suit your team, for £1,850 + VAT per delegate.

Is there a group discount?

Yes. 20% for 2 delegates, 35% for 3 delegates and 50% for 5 or more delegates on the same course.

Can the course be delivered in Arabic or another language?

Yes. Our courses can be delivered in English, Arabic or any European language, by a trainer who teaches in that language. Mention your preferred language when you register or enquire.

What certificate will I receive?

An ARUK Training Certificate of Attendance.

Related courses

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Machine Learning and Predictive Models

Key booking terms (read before you register)
  • The full fee is paid in advance, before the course starts.
  • Cancel in writing 30 or more days before the start date (45 for in-house) for a refund. Later than that, the full fee is payable.
  • No refund for non-attendance or partial attendance.
  • Places are for the named delegate only; another person cannot attend instead.
  • Moving to another date or city is only possible if ARUK agrees in writing.
  • Travel, accommodation and visas are your responsibility. A refused visa does not change the cancellation terms.

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Machine Learning and Predictive Models