Hands-on machine learning with JavaScript : solve complex computational web problems using machine learning / Burak Kanber.

Format:

Book

Author/Creator:

Kanber, Burak, author.

Language:

English

Subjects (All):

JavaScript (Computer program language).

Physical Description:

1 online resource (343 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt, 2018.

Biography/History:

Kanber Burak: Burak Kanber is an entrepreneur, software engineer, and the co-author of "Genetic Algorithms in Java". He earned his Bachelor's and Master's degrees in Mechanical Engineering from the prestigious Cooper Union in New York City, where he concentrated on software modeling and simulation of hybrid vehicle powertrains. Currently, Burak is a founder and the CTO of Tidal Labs, a popular enterprise influencer marketing platform. Previously, Burak had founded several startups, most notably a boutique design and engineering firm that helped startups and small businesses solve difficult technical problems. Through Tidal Labs, his engineering firm, and his other consulting work, Burak has helped design and produce dozens of successful products and has served as a technical advisor to many startups. Burak's core competencies are in machine learning, web technologies (specifically PHP and JavaScript), engineering (software, hybrid vehicles, control systems), product design and agile development. He's also worked on several interactive art projects, is a musician, and is a published engineer.

Summary:

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.

Contents:

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

Summary

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

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

Clustering

Classification

Regression

Dimensionality reduction

Optimization

Natural language processing

Image processing

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

Example 1 - Height, weight, and gender

Example 2 - Decolorizing a photo

Naive Bayes classifier

Tokenization

Building the algorithm

Example 3 - Movie review sentiment

Support Vector Machine

Random forest

Chapter 6: Association Rule Algorithms

The mathematical perspective

The algorithmic perspective

Association rule applications

Example - retail data

Chapter 7: Forecasting with Regression Algorithms

Regression versus classification

Regression basics

Example 1 - linear regression

Example 2 - exponential regression

Example 3 - polynomial regression

Other time-series analysis techniques

Filtering

Seasonality analysis

Fourier analysis

Chapter 8: Artificial Neural Network Algorithms

Conceptual overview of neural networks

Backpropagation training

Example - XOR in TensorFlow.js

Chapter 9: Deep Neural Networks

Convolutional Neural Networks

Convolutions and convolution layers

Example - MNIST handwritten digits

Recurrent neural networks

SimpleRNN

Gated recurrent units

Long Short-Term Memory

Chapter 10: Natural Language Processing in Practice

String distance

Term frequency - inverse document frequency

Tokenizing

Stemming

Phonetics

Part of speech tagging

Word embedding and neural networks

Chapter 11: Using Machine Learning in Real-Time Applications

Serializing models

Training models on the server

Web workers

Continually improving and per-user models

Data pipelines

Data querying

Data joining and aggregation

Transformation and normalization

Storing and delivering data

Chapter 12: Choosing the Best Algorithm for Your Application.

Mode of learning

The task at hand

Format, form, input, and output

Available resources

When it goes wrong

Combining models

Other Books You May Enjoy

Index.

Notes:

Description based on print version record.

ISBN:

9781788990301

1788990307

OCLC:

1039704668