Machine Learning Using Python: Theory, Algorithms, Practical Implementation, and Real-World Applications

Machine Learning Using Python: Theory, Algorithms, Practical Implementation, and Real-World Applications by Mrs. Renu Bala offers a comprehensive, beginner-friendly introduction to the concepts, techniques, and applications of machine learning. Designed for undergraduate and postgraduate students, researchers, and aspiring data scientists, the book bridges theoretical foundations with practical Python programming. Beginning with the fundamentals of machine learning, the book introduces essential mathematical and statistical concepts, Python programming, data visualisation, preprocessing, and feature engineering. It explores key algorithms, including linear and logistic regression, K-nearest neighbours, Naive Bayes, decision trees, ensemble methods, support vector machines, clustering, and principal component analysis. Readers also gain insights into model evaluation, cross-validation, hyperparameter tuning, neural networks, and deep learning. With practical code examples, original illustrations, tables, exercises, and worked projects, the book encourages hands-on learning and systematic problem-solving. Particular emphasis is placed on avoiding data leakage, selecting appropriate evaluation metrics, improving model generalisation, and understanding explainability, fairness, privacy, and responsible AI practices. Using widely adopted Python libraries such as NumPy, pandas, Matplotlib, Seaborn, and scikit-learn, this book provides a structured pathway from foundational knowledge to practical implementation. Whether used for classroom instruction, laboratory practice, self-study, or project development, it serves as a valuable resource for understanding machine learning and applying data-driven techniques to real-world challenges.