The Machine Learning for Demand Forecasting course in London teaches professionals how to leverage machine learning models to accurately predict demand and optimize supply chain operations.
From:
12-01-2026
From:
09-02-2026
From:
20-07-2026
From:
10-08-2026
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 London provide professionals with advanced knowledge and practical skills to leverage artificial intelligence and predictive analytics for accurate business planning. These programs are designed for data scientists, supply chain managers, business analysts, and operations leaders who want to enhance their ability to anticipate market demand, optimize inventory, and drive strategic decision-making using machine learning techniques.
Participants explore the fundamentals of machine learning in the context of demand forecasting, covering supervised and unsupervised models, time series analysis, regression techniques, and advanced algorithms such as neural networks and ensemble methods. The courses emphasize real-world applications, enabling participants to translate data insights into actionable forecasts that improve operational efficiency, reduce costs, and enhance customer satisfaction. Through hands-on exercises, case studies, and interactive simulations, attendees learn to preprocess data, evaluate model performance, and implement predictive models in business environments.
These demand forecasting and machine learning training programs in London also focus on the integration of forecasting models into broader business processes, including inventory management, supply chain planning, and sales strategy. Participants gain practical skills in data visualization, scenario analysis, and performance monitoring, ensuring that predictions are both accurate and strategically relevant. The curriculum highlights the balance between theoretical understanding and applied machine learning, equipping professionals to make data-driven decisions with confidence.
Attending these training courses in London offers a unique opportunity to engage with international experts and peers in a global business hub renowned for innovation and analytics. The city’s dynamic professional environment enhances the learning experience, allowing participants to explore diverse industry applications and emerging trends in AI-driven forecasting. By completing this specialization, participants will be prepared to leverage machine learning tools effectively, optimize demand planning, and contribute to organizational growth through precise, data-informed strategies.