Machine Learning: The Basics Author: Alexander Jung

Machine Learning: The Basics by Alexander Jung is a streamlined yet comprehensive introduction to the principles and practices of machine learning, designed for students, curious professionals, and researchers new to the field. Published by Springer as part of its respected “Machine Learning: Foundations, Methodologies, and Applications” series, the book lays out essential concepts in a logical, digestible format that balances mathematical rigor with intuitive explanation.

Jung navigates the reader through the landscape of learning algorithms, from simple linear models to more complex neural networks and ensemble techniques. Topics covered include:

Supervised and unsupervised learning paradigms

Overfitting and generalization

Feature selection and model evaluation

Deep learning foundations

Practical considerations for implementation and data preparation

The book emphasizes clarity and relevance, integrating examples that highlight real-world scenarios across industries such as finance, healthcare, and technology. It also touches on ethical concerns and the limitations of current models, helping readers build a critical and thoughtful understanding of machine learning’s role in modern decision-making.

With its network-themed cover art reflecting connectivity and complexity, Machine Learning: The Basics is both visually and intellectually inviting. It’s an ideal resource for those beginning their journey in artificial intelligence and for educators seeking to structure an approachable curriculum.