Machine Learning For Chemists: Practical Applications

Use Data Science to solve chemistry problems: predict molecular properties, classify compounds, and resolve spectra

What you'll learn

Understand the fundamentals of machine learning and how they apply to chemical problems. Confidently handle real-world chemical datasets for ML applications.

Select and implement supervised and unsupervised learning algorithms for chemistry. Apply regression, classification, and clustering methods to chemical data.

Interpret model performance using evaluation metrics relevant to chemical research. Visualize chemical datasets and model predictions effectively

Use Python libraries (scikit-learn, pandas, numpy) for ML tasks in chemistry, Apply ML to predict molecular properties such as Solubility, HOMO-LUMO gap

Gain practical skills to integrate ML into chemistry. Use ML-driven insights to support research publications and projects.

Separate the spectra (infra-red spectra) of each compound from a mixture of spectra using Machine learning approaches

Requirements

This course is designed for chemists and researchers who want to apply machine learning to chemical problems. To follow along smoothly, some basic knowledge of Python—such as variables, loops, functions, and working with libraries like NumPy or pandas is highly valuable. No advanced coding skills are required, but a beginner-level familiarity with Python will help you focus on the chemistry and machine learning concepts without getting stuck on programming basics. In case you are absolute beginner, i have added some videos by the end of the course where you can get bsic idea. Alternatively, i have a couple of courses on python for chemists which will guide you thoroughly on how to use python in chemistry

Description

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

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Machine Learning (Theory Lecture)

Lecture 3 Key Concepts of Machine Learning (Target, model training)

Lecture 4 Machine Learning Types, a graphical presentation

Section 2: Supervised Learning: Predicitn boiling points of alcohols through Machine Learni

Lecture 5 Regression method of supervised learning (Theory)

Lecture 6 Linear Regression example of Boiling Pont of alcohols explained

Lecture 7 Linear Regression Model equation

Lecture 8 Analyzing dataset of boiling points of alcohols in NotePad

Lecture 9 Placing the dataset file in Google Drive and opening Google Drive

Lecture 10 Setting up Google Colab Environment

Lecture 11 Mounting Google Drive in Colab

Lecture 12 Reading dataset of boiling point through Pandas and reading head.

Lecture 13 Deta Preprocessing approaches

Lecture 14 Cleaning the dataset before ML

Lecture 15 Feature Engineering

Lecture 16 Feature Selection

Lecture 17 plotting boiling point against possible features (no ML)

Lecture 18 Importing libraries for supervised machine learning

Lecture 19 Defining features and Target

Lecture 20 Completing the code for predicting boiling points

Lecture 21 Model evaluation Metrices (Theory)

Lecture 22 Evaluating the model and plotting the results

Lecture 23 Effect of test size on the performance of ML Model

Lecture 24 Graph of predicted vs actual boiling points

Lecture 25 Residual Plot

Lecture 26 Predicting bp of an unkonw alcohol

Lecture 27 Model II of Regression (Ridge Model): with standard Scalar for boiling Point

Lecture 28 Robust Scalar for Ridge Model and coefficients

Lecture 29 Outliers and How to handle them

Lecture 30 Model III: Lasso Model of Regression for predicing Boiling Points of Alchohols

Lecture 31 Cross Validation (Theory)

Lecture 32 K-fold Cross Validation In Machine Learning

Lecture 33 Scalars (minmax, standard and Robust) and effect on coefficients of features

Section 3: Predicting solubliity of compounds using Random Forest model of supervised ML

Lecture 34 Installing RdKit library in Google Colab

Lecture 35 Importing Libraries required for the problem

Lecture 36 Extracting features as descriptors for molecules using smiles in RdKit

Lecture 37 Loading dataset and preprocessing the data

Lecture 38 Grid search and the best grid values for the problem

Lecture 39 Model evaluation and the performance

Lecture 40 Plotting the results (feature importance, residual frequency, etc)

Section 4: Supervised Machine Learning for Classification of Compounds

Lecture 41 Supervised Learning Classification (Theory) I

Lecture 42 Supervised Learning Classification (Theory) II

Lecture 43 Classification of compounds example explained

Lecture 44 Predicting the classification of compounds (importing Libraries)

Lecture 45 Evaluation of the model throgh Confusion Matrix

Section 5: Unsupervised Learning: Part 1: Clustering of samples from Wine data

Lecture 46 Dimensionality Reduction (Principal component analysis PCA)

Lecture 47 Clustering example explained in theory

Lecture 48 Analyzing Wine data

Lecture 49 Unsupervised learning Clustering import libraries

Lecture 50 Clustering, Plotting PCA1 against PCA2

Lecture 51 Silhoutte Score

Lecture 52 DBSCAN Approach for clustering

Section 6: Unsupervised Learning for blind signal separation

Lecture 53 Blind Signal Separation example explanation

Lecture 54 FAST ICA 1: importing libraries and data of mixture spectrum in python

Lecture 55 FAST ICA II: Completing the code for independent component analysis

Lecture 56 Plotting two extracted spectrum from the mixture spectrum in parallel

Lecture 57 Plotting all extracted spectra from the mixture spectrum

Lecture 58 Identifying cyclohexane by comparing the extracted spectrum with the reference

Lecture 59 Identifying all components of the mixture by comparison with the reference spect

Lecture 60 Plotting all four spectra in one figure

Lecture 61 Plotting all four spectra in one figure (2nd approach)

Graduate students and researchers in chemistry or chemical engineering,Data scientists and ML engineers interested in scientific applications,Academic researchers wanting to integrate machine learning into chemical research,Computational chemistry professionals seeking to expand their ML toolkit,Laboratory managers implementing data-driven approaches to experimental design