Hands-on Machine Learning with JavaScript: Solve complex computational web problems using machine learning

Chapter 2: Data Exploration; An overview; Feature identification; The curse of dimensionality; Feature selection and feature extraction; Pearson correlation example; Cleaning and preparing data; Handling missing data; Missing categorical data; Missing numerical data; Handling noise; Handling outliers; Transforming and normalizing data; Summary; Chapter 3: Tour of Machine Learning Algorithms; Introduction to machine learning; Types of learning; Unsupervised learning; Supervised learning; Measuring accuracy; Supervised learning algorithms; Reinforcement learning; Categories of algorithms.

This book demonstrates various machine learning techniques and their implementation in JavaScript. Build models to power your applications with smart, predictive features. From predicting future prices, analyzing sentiments to medical diagnosis, this book shows you how to use the power of JavaScript to build efficient machine learning systems.

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Cover; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Exploring the Potential of JavaScript; Why JavaScript?; Why machine learning, why now?; Advantages and challenges of JavaScript; The CommonJS initiative; Node.js; TypeScript language; Improvements in ES6; Let and const; Classes; Module imports; Arrow functions; Object literals; The for ... of function; Promises; The async/await functions; Preparing the development environment; Installing Node.js; Optionally installing Yarn; Creating and initializing an example project; Creating a Hello World project.

ClusteringClassification; Regression; Dimensionality reduction; Optimization; Natural language processing; Image processing; Summary; Chapter 4: Grouping with Clustering Algorithms; Average and distance; Writing the k-means algorithm; Setting up the environment; Initializing the algorithm; Testing random centroid generation; Assigning points to centroids; Updating centroid locations; The main loop; Example 1 -- k-means on simple 2D data; Example 2 -- 3D data; k-means where k is unknown; Summary; Chapter 5: Classification Algorithms; k-Nearest Neighbor; Building the KNN algorithm.

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