The Machine Learning for Demand Forecasting in Paris is a technical training course applying AI to enhance demand planning accuracy.
From:
24-08-2026
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 Paris provide professionals with the essential skills to apply machine learning techniques to predict demand patterns and optimize inventory management, supply chain strategies, and customer satisfaction. Designed for data scientists, business analysts, supply chain managers, and decision-makers, these programs focus on using machine learning algorithms to enhance demand forecasting accuracy and improve operational efficiency.
Participants will explore the core concepts of demand forecasting and how machine learning models can be applied to predict future demand trends based on historical data, market conditions, and other external factors. The courses cover a range of techniques including time series analysis, regression models, and neural networks, as well as advanced methods like ensemble learning and reinforcement learning. Through practical exercises and real-world case studies, attendees will learn how to implement these models to generate reliable demand forecasts, reduce stockouts, minimize excess inventory, and enhance overall supply chain performance.
These machine learning for demand forecasting training programs in Paris offer a hands-on approach, with a focus on using industry-standard tools and programming languages such as Python, R, and TensorFlow. Participants will gain experience in building, training, and fine-tuning machine learning models, and integrating these models into existing business systems for real-time demand prediction. The curriculum also covers data preparation, feature engineering, model evaluation, and performance optimization, ensuring that participants can build robust forecasting systems that align with organizational goals.
Attending these training courses in Paris provides a unique opportunity to engage with experts in machine learning, collaborate with professionals from diverse industries, and immerse oneself in Paris’ dynamic tech ecosystem. By completing this specialization, participants will be equipped to implement machine learning-driven demand forecasting systems that optimize inventory management, reduce costs, and improve decision-making processes in today’s fast-paced business environment.