Fundamentals of Machine Learning for Supply Chain

Rajvir Dua is the Head of Data Science at LearnQuest, where he combines his expertise in data science with a strong interest in supply chain management. With experience as a teaching and research assistant, he has been actively involved in the data science and consulting startup space. His academic interests are particularly focused on economic problems related to game theory and optimization, which inform his approach to data-driven decision-making in supply chain contexts.On Coursera, Rajvir offers a variety of courses designed to enhance learners' understanding of data science and its applications. His courses include Advanced AI Techniques for the Supply Chain, Fundamentals of Machine Learning for Supply Chain, and Demand Forecasting Using Time Series. These courses aim to equip students with practical skills in machine learning and data analysis, preparing them for challenges in modern supply chain management. Rajvir's commitment to education and his extensive knowledge in data science make him a valuable resource for those looking to advance their careers in this dynamic field.

Neelesh Tiruviluamala is a math professor and the co-founder and CEO of Machine Learning Solutions, with over ten years of consulting experience in the machine learning domain. He has a keen interest in supply chain problems, appreciating the rich data sets that allow for the application of various quantitative tools. His expertise bridges the gap between theoretical mathematics and practical machine learning applications, making him a valuable asset in the field.On Coursera, Neelesh offers a range of courses that focus on advanced machine learning techniques and their applications in supply chain management. His courses include Advanced AI Techniques for the Supply Chain, Demand Forecasting Using Time Series, and Introduction to Data Science and scikit-learn in Python. Through these courses, he aims to equip learners with the skills needed to leverage data science effectively in real-world scenarios, particularly within the context of supply chain optimization. Neelesh's commitment to education and his extensive experience make him an influential figure in the intersection of mathematics and machine learning.