Supervised Learning Algorithms and Techniques Course

Master supervised learning for prediction and classification, including validation, model selection, interpretation and responsible deployment.

Artificial Intelligence and Machine Learning Courses Confirmed

Training Locations

This Supervised Learning Algorithms and Techniques Course is available in multiple cities. Please select your preferred location from the list below

Durrës

Albania

Tirana

Albania

Andorra la Vella

Andorra

Escaldes-Engordany

Andorra

Innsbruck

Austria

Salzburg

Austria

Vienna

Austria

Gomel

Belarus

Minsk

Belarus

Antwerp

Belgium

Brussels

Belgium

Banja Luka

Bosnia and Herzegovina

Sarajevo

Bosnia and Herzegovina

Plovdiv

Bulgaria

Sofia

Bulgaria

Dubrovnik

Croatia

Split

Croatia

Zagreb

Croatia

Limassol

Cyprus

Nicosia

Cyprus

Brno

Czech Republic

Prague

Czech Republic

Aarhus

Denmark

Copenhagen

Denmark

Tallinn

Estonia

Tartu

Estonia

Helsinki

Finland

Tampere

Finland

Lyon

France

Marseille

France

Nice

France

Paris

France

Berlin

Germany

Frankfurt

Germany

Hamburg

Germany

Munich

Germany

Athens

Greece

Thessaloniki

Greece

Budapest

Hungary

Debrecen

Hungary

Akureyri

Iceland

Reykjavík

Iceland

Cork

Ireland

Dublin

Ireland

Florence

Italy

Milan

Italy

Naples

Italy

Rome

Italy

Pristina

Kosovo

Prizren

Kosovo

Liepāja

Latvia

Riga

Latvia

Schaan

Liechtenstein

Vaduz

Liechtenstein

Kaunas

Lithuania

Vilnius

Lithuania

Esch-sur-Alzette

Luxembourg

Luxembourg City

Luxembourg

St. Julian's

Malta

Valletta

Malta

Bălți

Moldova

Chișinău

Moldova

La Condamine

Monaco

Monte Carlo

Monaco

Budva

Montenegro

Podgorica

Montenegro

Amsterdam

Netherlands

Rotterdam

Netherlands

The Hague

Netherlands

Ohrid

North Macedonia

Skopje

North Macedonia

Bergen

Norway

Oslo

Norway

Gdańsk

Poland

Kraków

Poland

Warsaw

Poland

Faro

Portugal

Lisbon

Portugal

Porto

Portugal

Bucharest

Romania

Cluj-Napoca

Romania

City of San Marino

San Marino

Serravalle

San Marino

Belgrade

Serbia

Novi Sad

Serbia

Singapore

Bratislava

Slovakia

Košice

Slovakia

Bled

Slovenia

Ljubljana

Slovenia

Barcelona

Spain

Madrid

Spain

Valencia

Spain

Gothenburg

Sweden

Stockholm

Sweden

Bern

Switzerland

Geneva

Switzerland

Zurich

Switzerland

Kyiv

Ukraine

Lviv

Ukraine

Odesa

Ukraine

Dubai

United Arab Emirates

Birmingham

United Kingdom

Edinburgh

United Kingdom

London

United Kingdom

Manchester

United Kingdom

Rome (Vatican-adjacent)

Vatican City

Training Outlines

Introduction

Supervised learning supports high-value organisational decisions such as demand forecasting, credit assessment, customer retention, fraud detection and quality prediction. A useful model must do more than fit historical data: it must reflect the real decision, avoid leakage, generalise to new cases and measure errors according to their operational consequences.

This five-day course develops practical competence in designing, training and evaluating supervised-learning models. Participants will work with regression, classification and ensemble methods; establish defensible baselines; select meaningful performance measures; and interpret results for business use. The course combines technical reasoning with realistic prediction cases and controlled model comparisons.

Objectives

By the end of this course, participants will be able to:

Formulate regression and classification problems correctly.
Prepare valid training, validation and test data.
Compare major supervised-learning algorithms.
Control model complexity through regularisation.
Select metrics that reflect operational error costs.
Optimise models without contaminating evaluation.
Interpret predictions and communicate limitations.
Design a deployable supervised-learning workflow.

Course Content

Day 1: Prediction Problems and Model Design

Regression versus classification decisions
Targets, features and prediction horizons
Training, validation and test strategy
Target and temporal leakage
Statistical and business baselines
Prediction-design workshop

Day 2: Regression Methods and Evaluation

Linear regression and model assumptions
Feature interactions and transformations
Ridge, Lasso and Elastic Net
Tree-based regression methods
Regression metrics and error analysis
Operational forecasting laboratory

Day 3: Classification and Decision Thresholds

Logistic regression and probability estimation
Decision trees and support-vector methods
Precision, recall, specificity and F1
ROC and precision–recall analysis
Class imbalance and error costs
Decision-threshold laboratory

Day 4: Ensembles and Model Selection

Bagging and random forests
Gradient boosting strategies
Hyperparameter search and cross-validation
Feature selection and importance
Global and local interpretation
Controlled model-comparison exercise

Day 5: Robust Evaluation and Deployment

Out-of-time and segment evaluation
Calibration and performance stability
Structured model-error analysis
Production scoring and fallback rules
Drift monitoring and retraining criteria
Capstone supervised-learning solution

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Supervised Learning Algorithms and Techniques Course. Please review the schedule to find the most convenient option for you. You can also use the below search bar to type the city name and filter the results.