Name: Hands-On Machine Learning with JavaScript
Author: Burak Kanber
ISBN: 9781788990301

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Hands-On Machine Learning with JavaScript

Burak Kanber

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Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
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
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
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
Summary
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
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
Summary
Association Rule Algorithms
The mathematical perspective
The algorithmic perspective
Association rule applications
Example – retail data
Summary
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
Summary
Artificial Neural Network Algorithms
Conceptual overview of neural networks
Backpropagation training
Example - XOR in TensorFlow.js
Summary
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
Summary
Natural Language Processing in Practice
String distance
Term frequency - inverse document frequency
Tokenizing
Stemming
Phonetics
Part of speech tagging
Word embedding and neural networks
Summary
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
Summary
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
Summary
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Hands-On Machine Learning with JavaScript