Machine Learning for Chemists: Practical Applications

Machine Learning is transforming the way chemists analyze data, predict properties, and accelerate discoveries in various sub-disciplines of Chemistry. This course is designed to give you the practical skills and confidence to apply machine learning directly to chemical problems.Starting from the basics, you will learn how to preprocess chemical datasets, explore molecular descriptors, and choose the right algorithms for prediction and classification. Step by step, you will apply regression, classification, and clustering methods to real-world chemical examples. You will gain hands-on experience with Python libraries such as NumPy, pandas, matplotlib, and scikit-learn—without being overwhelmed by unnecessary theory.Throughout the course, we’ll connect machine learning concepts to chemistry applications: predicting molecular properties (solubility) , analyzing spectra, classifying compounds, resolving complex spectra of mixture to individual spectra and grouping similar molecules. By the end, you will not only understand how these algorithms work but also know how to implement them for your own research and projects.This is not just a programming course—it’s a transformation in how you approach chemistry. You will leave with a solid foundation in applying ML to chemical data and the ability to contribute to modern, data-driven research.Note: Basic knowledge of Python is recommended for this course