Executive Development Programme in Machine Learning for Demand Forecasting

This course is tailored for executives, data science leaders, and business managers who seek to harness machine learning techniques for enhancing demand forecasting. Participants will gain a deep understanding of ML fundamentals, practical skills in using advanced models, and insights into integrating these tools for strategic business decisions.

By the end, attendees will be able to lead and manage ML-driven forecasting initiatives, interpret complex data patterns, and leverage predictive analytics to optimize supply chain operations, reduce inventory costs, and enhance customer satisfaction.

1. 1. Introduction to Machine Learning for Demand Forecasting: Learners will explore the basics of machine learning and its application in demand forecasting, gaining an understanding of key concepts and terminology. They will learn how to build and evaluate simple predictive models.
2. 2. Data Preprocessing and Feature Engineering: This module focuses on preparing data for machine learning models, including data cleaning, transformation, and feature selection techniques that enhance model accuracy and efficiency.
3. 3. Time Series Analysis Fundamentals: Learners will delve into the analysis of time series data, understanding trends, seasonality, and cyclic patterns, and gaining skills in decomposition and smoothing techniques.
4. 4. Building Linear Models for Forecasting: This module covers the construction and interpretation of linear regression models for forecasting, including multiple linear regression and understanding the impact of multicollinearity.
5. 5. Advanced Regression Techniques: Learners will explore advanced regression models such as ridge regression, lasso regression, and elastic net, learning how to handle overfitting and model selection.
6. 6. Introduction to Machine Learning Algorithms: This module introduces various machine learning algorithms relevant to demand forecasting, including decision trees, random forests, and support vector machines.
7. 7. Evaluating and Comparing Models: Learners will learn how to evaluate and compare different machine learning models using metrics like MAE, RMSE, and AIC, and understand the importance of cross-validation in model selection.
8. 8. Neural Networks and Deep Learning for Forecasting: This module covers the application of neural networks and deep learning techniques for demand forecasting, including building and training multi-layer perceptrons and recurrent neural networks.
9. 9. Handling Seasonality and Trends in Forecasting: Learners will study advanced techniques for handling seasonality and trends in time series data, including seasonal decomposition of time series and trend analysis.
10. 10. Implementing and Deploying Forecasting Models: In this final module, learners will gain hands-on experience in implementing and deploying machine learning models for demand forecasting in real-world scenarios, including model validation and performance monitoring.