Data-driven decision-making is a key competitive advantage in today's business landscape. This course provides a foundational introduction to machine learning techniques and algorithms, equipping students with analytical and software tools to extract insights and create value from data. The course mainly focuses on predictive analytics methods, including linear models, decision trees, nearest neighbor classifiers, boosting, random forests, neural networks, probabilistic models, text analysis, complexity control, regularization, and model evaluation. Additionally, students will explore unsupervised learning techniques such as clustering, collaborative filtering, and dimensionality reduction, with applications in customer segmentation and recommender systems. Through real-world business examples, students will gain hands-on experience using Python in applying machine learning to marketing, forecasting and operations domain.