Data Preparation and Feature Engineering for Machine Learning Course

Master data preparation and feature engineering, including quality, leakage prevention, encoding, selection and production-ready ML pipelines.

Artificial Intelligence and Machine Learning Courses Confirmed

Training Locations

This Data Preparation and Feature Engineering for Machine Learning 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

Machine-learning performance is often determined before an algorithm is selected. Incomplete records, inconsistent definitions, unstable categories and hidden leakage can produce impressive development results that fail in real operations. Data preparation and feature engineering therefore require analytical judgement, domain knowledge and strict control of how information becomes available.

This five-day course gives professionals a rigorous, practical method for converting raw organisational data into reliable machine-learning inputs. Participants will assess data provenance and quality, prevent leakage, construct meaningful features, select appropriate representations and build reproducible pipelines that remain consistent from training to production. Applied exercises use realistic data problems rather than idealised classroom datasets.

Objectives

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

Define modelling data, labels and timing requirements.
Assess provenance, quality and representativeness.
Resolve missingness, outliers and inconsistent records.
Prevent target, temporal and preprocessing leakage.
Engineer valid numerical, categorical and temporal features.
Apply encoding, selection and reduction techniques.
Build reproducible training and inference pipelines.
Monitor and govern production feature behaviour.

Course Content

Day 1: Data Requirements and Readiness

Prediction targets and units of analysis
Observation windows and feature availability
Data provenance and lineage
Granularity, keys and reliable joins
Population and sampling bias
Data-readiness assessment workshop

Day 2: Data Quality and Leakage Prevention

Systematic data profiling
Missing-data patterns and treatment
Outlier analysis and validation
Duplicates and entity resolution
Target and temporal leakage
Flawed-dataset audit laboratory

Day 3: Practical Feature Engineering

Numerical scaling and transformation
Categorical encoding strategies
High-cardinality feature control
Dates, lags and rolling windows
Text, geographic and interaction features
Temporal feature-building laboratory

Day 4: Feature Selection and Representation

Filter, wrapper and embedded selection
Regularisation-based feature selection
Multicollinearity and redundancy
Principal component analysis
Embeddings and learned representations
Feature-value comparison experiment

Day 5: Production Pipelines and Governance

Reproducible preprocessing pipelines
Training and inference consistency
Feature stores and point-in-time accuracy
Automated validation and testing
Feature drift and quality monitoring
Capstone production-pipeline design

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Data Preparation and Feature Engineering for Machine Learning 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.