Introduction to Machine Learning in Pharmaceutical Sciences (Theory)

About This Book

This book has been developed to provide undergraduate pharmacy students with a clear and structured introduction to the fundamental concepts of machine learning and their relevance to pharmaceutical sciences. The objective is not merely to introduce computational terminology, but to help students understand how data-driven approaches can be applied to solve real-world pharmaceutical problems.

The book begins with the basic concepts of artificial intelligence, machine learning, datasets, variables, and data preprocessing before progressing towards important learning approaches and commonly used machine-learning techniques. Concepts such as supervised and unsupervised learning, classification, regression, clustering, model evaluation, feature selection, and basic predictive modelling are presented in a systematic manner. Wherever appropriate, pharmaceutical examples and applications are incorporated to connect theoretical concepts with practical areas of pharmacy.

The five syllabus units — Foundations of Machine Learning, Regression Models in Healthcare, Classification Models in Clinical Applications, Tree-Based Models & Ensemble Learning, and Unsupervised Learning & Practical Case Studies — are covered across ten chapters. Each chapter carries learning outcomes, a common-misconceptions note, a quick recall, a chapter summary, self-assessment and references. The book closes with a glossary, a formula sheet, an illustrative model question paper, an index and a consolidated reference list.

Table of Contents

1. Artificial Intelligence, Machine Learning and Data Science: Scope and Types of Learning
2. Building a Model: Data, Training, Testing and the Bias–Variance Trade-off
3. Linear Regression: Fitting a Line to Pharmaceutical Data
4. Multiple Regression, Goodness-of-Fit and Regression in Practice
5. Logistic Regression and the Confusion Matrix
6. ROC Curves, AUC and k-Nearest Neighbours in Clinical Prediction
7. Decision Trees: Structure, Splitting and Interpretation
8. Random Forest and Ensemble Learning
9. Unsupervised Learning and K-means Clustering
10. Integrated Case Studies: Patient Segmentation and Drug Grouping

Supported by a Glossary, Formula Sheet, Model Question Paper, Index and Consolidated References.

Keywords

How to Cite

Surwade, K. S., Zambare, Y. B., & Swamy, M. R. (2026). Introduction to Machine Learning in Pharmaceutical Sciences (Theory) (1st ed.). WiseLeaf Scientific Ventures. ISBN: 978-81-180383-6-8