Table Of ContentHands-On Machine Learning
with JavaScript
Solve complex computational web problems
using machine learning
Burak Kanber
BIRMINGHAM - MUMBAI
Hands-On Machine Learning with JavaScript
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Contributors
About the author
Burak Kanber is an entrepreneur and engineer with nearly 20 years of experience
developing software. After completing graduate school at The Cooper Union, Burak joined
a marketing technology startup as co-founder and CTO. Machine learning was not as
accessible as it is today, so from 2010–2012 he wrote a series of articles that taught ML to
web developers from first principles. His ML in JS series has since been enjoyed by over a
million readers.
Burak is also the co-author of Genetic Algorithms in Java Basics, published by Apress in 2015.
I have been writing about ML in JS for years, and I would like to thank Packt and their
team for the opportunity to bring this book to market. I would also like to thank my wife
Kimberly for her endless support, love, and patience, especially during the countless hours
I spent in my own little world writing this book. I'll never stop trying to make you proud.
About the reviewer
Paulo Pires is a geographical engineer who, in his early thirties, decided to apply his skills
in business intelligence. He started working in Webdetails seven years ago and specialized
in frontend dashboards with CTools, Pentaho, HTML, JavaScript and its libraries, CSS, and
some backend ETL. Currently, he is a senior consultant.
As a hobby, he likes to explore new technologies, and lately he has been playing with
Home Automation with Node.js, Sonoff sensors, and Mosquitto broker.
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Table of Contents
Preface 1
Chapter 1: Exploring the Potential of JavaScript 8
Why JavaScript? 8
Why machine learning, why now? 11
Advantages and challenges of JavaScript 13
The CommonJS initiative 14
Node.js 15
TypeScript language 17
Improvements in ES6 18
Let and const 19
Classes 20
Module imports 21
Arrow functions 22
Object literals 24
The for...of function 25
Promises 25
The async/await functions 26
Preparing the development environment 27
Installing Node.js 28
Optionally installing Yarn 28
Creating and initializing an example project 29
Creating a Hello World project 29
Summary 32
Chapter 2: Data Exploration 33
An overview 33
Feature identification 36
The curse of dimensionality 37
Feature selection and feature extraction 39
Pearson correlation example 42
Cleaning and preparing data 45
Handling missing data 46
Missing categorical data 46
Missing numerical data 47
Handling noise 47
Handling outliers 53
Transforming and normalizing data 57
Summary 64
Table of Contents
Chapter 3: Tour of Machine Learning Algorithms 65
Introduction to machine learning 66
Types of learning 66
Unsupervised learning 68
Supervised learning 71
Measuring accuracy 72
Supervised learning algorithms 75
Reinforcement learning 79
Categories of algorithms 80
Clustering 80
Classification 81
Regression 81
Dimensionality reduction 81
Optimization 82
Natural language processing 82
Image processing 83
Summary 83
Chapter 4: Grouping with Clustering Algorithms 84
Average and distance 86
Writing the k-means algorithm 88
Setting up the environment 89
Initializing the algorithm 90
Testing random centroid generation 95
Assigning points to centroids 97
Updating centroid locations 99
The main loop 103
Example 1 – k-means on simple 2D data 104
Example 2 – 3D data 111
k-means where k is unknown 114
Summary 120
Chapter 5: Classification Algorithms 121
k-Nearest Neighbor 122
Building the KNN algorithm 123
Example 1 – Height, weight, and gender 128
Example 2 – Decolorizing a photo 132
Naive Bayes classifier 137
Tokenization 140
Building the algorithm 141
Example 3 – Movie review sentiment 151
Support Vector Machine 156
Random forest 165
Summary 171
[ ii ]
Table of Contents
Chapter 6: Association Rule Algorithms 172
The mathematical perspective 174
The algorithmic perspective 177
Association rule applications 179
Example – retail data 181
Summary 186
Chapter 7: Forecasting with Regression Algorithms 187
Regression versus classification 188
Regression basics 189
Example 1 – linear regression 194
Example 2 – exponential regression 198
Example 3 – polynomial regression 204
Other time-series analysis techniques 207
Filtering 208
Seasonality analysis 211
Fourier analysis 213
Summary 215
Chapter 8: Artificial Neural Network Algorithms 216
Conceptual overview of neural networks 217
Backpropagation training 222
Example - XOR in TensorFlow.js 225
Summary 234
Chapter 9: Deep Neural Networks 235
Convolutional Neural Networks 236
Convolutions and convolution layers 237
Example – MNIST handwritten digits 242
Recurrent neural networks 249
SimpleRNN 250
Gated recurrent units 255
Long Short-Term Memory 257
Summary 260
Chapter 10: Natural Language Processing in Practice 261
String distance 263
Term frequency - inverse document frequency 265
Tokenizing 271
Stemming 278
Phonetics 280
Part of speech tagging 282
Word embedding and neural networks 284
Summary 286
[ iii ]
Table of Contents
Chapter 11: Using Machine Learning in Real-Time Applications 288
Serializing models 289
Training models on the server 290
Web workers 292
Continually improving and per-user models 294
Data pipelines 296
Data querying 297
Data joining and aggregation 300
Transformation and normalization 301
Storing and delivering data 305
Summary 307
Chapter 12: Choosing the Best Algorithm for Your Application 309
Mode of learning 311
The task at hand 314
Format, form, input, and output 316
Available resources 318
When it goes wrong 320
Combining models 322
Summary 324
Other Books You May Enjoy 326
Index 329
[ iv ]
