The Machine Learning for Demand Forecasting course in Amsterdam is a specialized training course that equips professionals to leverage machine learning techniques for accurate demand predictions.
From:
16-02-2026
From:
15-06-2026
From:
31-08-2026
From:
14-09-2026
Course Overview
Accurate demand forecasting is vital for supply chain efficiency, inventory optimization, and strategic planning. This Machine Learning for Demand Forecasting Training Course introduces participants to modern ML techniques that outperform traditional forecasting methods.
Participants will learn how to build and evaluate forecasting models, use time-series analysis, and apply supervised and unsupervised learning approaches. Real-world case studies and practical labs will show how organizations leverage ML to anticipate demand, reduce costs, and improve decision-making.
By the end of the course, attendees will be able to design and implement machine learning models that deliver more reliable demand forecasts and support agile business strategies.
Course Benefits
Improve demand forecasting accuracy with ML
Apply predictive analytics for smarter planning
Optimize supply chain and inventory management
Anticipate customer demand and market fluctuations
Strengthen decision-making with AI-driven insights
Course Objectives
Explore machine learning applications in demand forecasting
Build time-series and regression-based forecasting models
Apply supervised and unsupervised ML techniques
Evaluate and validate model performance
Use ML for supply chain and sales demand predictions
Address data quality and feature engineering challenges
Integrate ML forecasting into business planning systems
Training Methodology
The course combines lectures, case studies, and hands-on labs with forecasting datasets. Participants will build and test ML models using real-world scenarios and platforms.
Target Audience
Supply chain and operations managers
Data scientists and analysts
Business strategists and planners
Professionals in retail, manufacturing, and logistics
Target Competencies
Machine learning forecasting techniques
Predictive analytics for demand planning
Time-series modeling and evaluation
Data-driven supply chain strategy
Course Outline
Unit 1: Introduction to ML in Forecasting
Traditional vs. machine learning forecasting methods
Benefits and challenges of ML in demand planning
Key ML algorithms for forecasting
Industry case studies
Unit 2: Data Preparation and Feature Engineering
Collecting and cleaning demand data
Handling missing values and outliers
Feature engineering for better predictions
Practical dataset preparation exercise
Unit 3: Time-Series and Predictive Modeling
Time-series analysis and ARIMA models
Regression and neural network approaches
Hybrid models for complex forecasting
Building predictive models in practice
Unit 4: Model Evaluation and Validation
Metrics for forecasting accuracy
Cross-validation and testing approaches
Avoiding overfitting and underfitting
Real-world model evaluation case study
Unit 5: Business Integration and Future of ML Forecasting
Embedding ML forecasts into supply chain planning
Using forecasts for sales and inventory optimization
Ethical and governance considerations in AI forecasting
Future trends in demand forecasting technologies
Ready to improve forecasting with machine learning?
Join the Machine Learning for Demand Forecasting Training Course with EuroQuest International Training and transform the accuracy of your business planning.
The Machine Learning for Demand Forecasting Training Courses in Amsterdam provide professionals with the analytical and technical expertise to predict market demand, optimize inventory, and improve strategic planning through data-driven insights. Designed for data scientists, supply chain managers, business analysts, and operations leaders, these programs focus on applying advanced machine learning models to enhance forecasting accuracy and business performance.
Participants gain a strong foundation in demand forecasting methodologies, exploring how machine learning techniques such as regression, time series analysis, neural networks, and ensemble models can uncover patterns in historical data. The courses emphasize practical model development—covering data preprocessing, feature selection, and performance evaluation—to build accurate forecasting systems. Through real-world case studies and interactive workshops, attendees learn to apply predictive analytics in sectors such as retail, manufacturing, logistics, and finance.
These machine learning and forecasting training programs in Amsterdam combine technical depth with business relevance. Participants explore how to integrate forecasting models with enterprise systems, automate prediction pipelines, and translate analytical outputs into actionable strategies. The curriculum also covers demand sensing, scenario simulation, and the role of external data sources—such as economic indicators and consumer trends—in refining forecast precision.
Attending these training courses in Amsterdam provides professionals with access to international experts and peers in a leading global center for innovation and data science. The city’s forward-looking business and technology ecosystem offers an ideal environment for mastering predictive modeling and intelligent planning. By completing this specialization, participants will be equipped to implement machine learning solutions that enhance forecasting accuracy, reduce operational risks, and support agile, data-driven decision-making—strengthening organizational competitiveness in today’s dynamic global marketplace.