Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications

Machine learning depends on more than algorithms: it also draws on mathematics, statistics, well-prepared data, and the tools used to put ideas into practice. This edited volume brings those strands together, tracing a route from foundational concepts to big-data applications and case studies.

Across 17 chapters in five sections, the book combines theoretical groundwork with discussions of algorithms, software tools, and examples from fields including finance, healthcare, agriculture, and cybersecurity. Its breadth makes it a useful reference for readers who want to see how machine learning and big data connect across disciplines.

Start with the foundations

The opening chapters establish essential mathematical and statistical ideas, including linear algebra, calculus, probability, correlation, and regression. Readers then encounter the practical work that comes before modeling: preprocessing data through cleaning, integration, transformation, and reduction. This foundation helps put later discussions of algorithms into context.

From big data to machine-learning methods

The book examines big-data architecture, analytics tools, and challenges associated with the volume, variety, and velocity of data. Its machine-learning coverage spans supervised and unsupervised approaches as well as reinforcement learning, with algorithms and applications developed across multiple chapters.

Tools, examples, and applied questions

Hands-on chapters introduce tools including Apache Mahout and H2O. Case studies and application discussions extend the material into areas such as banking, healthcare, the Internet of Things, and intrusion detection. The result is a cross-disciplinary perspective on how data, methods, and practical requirements meet in real-world settings.

Who may find it useful?

Researchers, students, academic and industrial developers, and practitioners interested in machine learning or big-data analytics will find a wide-ranging collection of foundations and applications. Because the book is edited and multi-authored, its chapters offer distinct perspectives rather than a single continuous tutorial.

For readers looking to connect core concepts with tools and varied applications, Machine Learning and Big Data offers a broad map of the field.