Machine Learning: Hands-On for Developers and Technical Professionals, Second Edition

Machine learning becomes easier to put to work when its algorithms are connected to real development tasks. Jason Bell’s second edition takes that practical route, guiding readers from data preparation and project planning through a range of established learning methods and tools.

Written for developers and technical professionals, the book pairs explanations of machine-learning approaches with working examples. Its progression—from core concepts to text, image, and streaming-data applications—helps readers see how the methods relate to the systems they build.

Start with the data, then choose the method

The opening chapters establish what machine learning is and how to plan for it, with attention to acquiring data and working with statistics, linear regression, and randomness. That groundwork gives readers a useful frame for evaluating algorithms: what kind of problem they address and what data is needed to make them useful.

A practical tour of machine-learning algorithms

Bell covers decision trees, clustering, association-rule learning, support vector machines, and artificial neural networks. Rather than treating machine learning as one technique, the book examines a range of approaches and how they can be applied in different settings.

• Build familiarity with data acquisition and preparation.
• Explore several major learning algorithms and their uses.
• See machine-learning applications involving text and images.
• Follow chapters on streaming machine learning with Kafka, Apache Spark, and R.

From core concepts to data in motion

The later chapters extend the discussion beyond static examples to streaming data and larger-scale tools. Supplementary appendices cover a Kafka quick start, Twitter API application configuration, useful Unix commands, and further reading—practical reference points alongside the main chapters.

For developers and technical professionals

This hands-on machine learning book is suited to software developers, data-focused technical professionals, and students who want a guided introduction to methods used in data science. Its coverage also makes it useful as a reference when comparing algorithms and considering how to bring machine learning into a working project.

Explore the second edition of Machine Learning: Hands-On for Developers and Technical Professionals for a grounded, application-oriented look at learning from data. 💻