Hands-On Machine Learning with JavaScript: Solve Complex Computational Web Problems Using Machine Learning

Machine learning need not be a detour away from JavaScript. This hands-on guide shows how to explore data, build models, and connect machine-learning methods with JavaScript applications, moving from foundational concepts to practical implementations.

Burak Kanber takes readers through a broad selection of approaches, from clustering and classification to neural networks and natural language processing. The focus is not just on what an algorithm is called, but on how to work with data, implement a method, and decide whether it suits the problem at hand.

Build understanding one algorithm at a time 💻

The chapters introduce key machine-learning ideas and then turn to concrete techniques. Topics include k-means clustering, k-nearest neighbors, Naive Bayes, support vector machines, random forests, association rules, and regression. Examples range from sentiment analysis and retail data to image-related tasks and handwritten-digit recognition.

From data preparation to useful models

Before a model can be useful, its data needs attention. The book covers feature selection, missing values, noise, outliers, and normalization, giving readers context for the work that happens before training. It also introduces ways to evaluate models and compare learning approaches.

Neural networks, language, and live applications

Later chapters explore neural networks and deep-learning architectures, including convolutional and recurrent networks, with a example. The natural language processing section addresses techniques such as tokenization, stemming, term frequency–inverse document frequency, and word embeddings. The closing material turns to real-time applications, data pipelines, and choosing or combining algorithms for a task.

A practical starting point for JavaScript developers

With working knowledge of JavaScript expected, this book is especially relevant to developers who want to explore machine learning without first changing languages. Its progression from data handling and algorithm fundamentals to application decisions makes it a useful bridge between established JavaScript skills and a wider set of machine-learning ideas.