Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks and assignments. Python, one of the most popular programming languages, is open source, free to use, and has plenty of learning resources. The workshop is designed for students without previous experience in programming, and it aims for a deeper understanding of the complexity of concepts in programming and machine learning. The examples used correspond to real data from physicochemical characterizations of wine, a content that is of interest for students. The contents of the workshop are introduction to Python, basic statistics, data visualization, and dimension reduction, classification, and regression.

Machine Learning, a subdomain of Artificial intelligence, is a pervasive technology that would mold how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a course that introduces machine learning for chemistry students based on a set of Python Notebooks and assignments. Python language, one of the most popular programming languages, allows for free software and resources, which ensures availability. The course is constructed for students without previous experience in programming, leading to an incremental progression in depth and complexity that covers both programming and machine learning concepts. The examples used are related to real data from physicochemical characterizations of wines, producing an attractive material that captures the interest of students. Topics included are Introduction to Python, Basic Statistics, Data Visualization and Dimension Reduction, Classification, and Regression.

Machine Learning, a subdomain of Artificial intelligence, is a pervasive technology that would mold how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a course that introduces machine learning for chemistry students based on a set of Python Notebooks and assignments. Python language, one of the most popular programming languages, allows for free software and resources, which ensures availability. The course is constructed for students without previous experience in programming, leading to an incremental progression in depth and complexity that covers both programming and machine learning concepts. The examples used are related to real data from physicochemical characterizations of wines, producing an attractive material that captures the interest of students. Topics included are Introduction to Python, Basic Statistics, Data Visualization and Dimension Reduction, Classification, and Regression.