Book Image

Hands-on Machine Learning with JavaScript

Book Image

Hands-on Machine Learning with JavaScript

Overview of this book

In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications.

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Exploring the Potential of JavaScript
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
Preparing the development environment
Summary
2
Data Exploration
Data Exploration
An overview
Feature identification
Cleaning and preparing data
Summary
3
Tour of Machine Learning Algorithms
Tour of Machine Learning Algorithms
Introduction to machine learning
Types of learning
Categories of algorithms
Summary
4
Grouping with Clustering Algorithms
Grouping with Clustering Algorithms
Average and distance
Writing the k-means algorithm
Example 1 – k-means on simple 2D data
Example 2 – 3D data
k-means where k is unknown
Summary
5
Classification Algorithms
Classification Algorithms
k-Nearest Neighbor
Naive Bayes classifier
Support Vector Machine
Random forest
Summary
6
Association Rule Algorithms
Association Rule Algorithms
The mathematical perspective
The algorithmic perspective
Association rule applications
Example – retail data
Summary
7
Forecasting with Regression Algorithms
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
Summary
8
Artificial Neural Network Algorithms
Artificial Neural Network Algorithms
Conceptual overview of neural networks
Backpropagation training
Example - XOR in TensorFlow.js
Summary
9
Deep Neural Networks
Deep Neural Networks
Convolutional Neural Networks
Recurrent neural networks
Summary
10
Natural Language Processing in Practice
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
11
Using Machine Learning in Real-Time Applications
Using Machine Learning in Real-Time Applications
Serializing models
Data pipelines
Summary
12
Choosing the Best Algorithm for Your Application
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
13
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Data Exploration

The single most important thing for a beginner to know about machine learning (ML) is that machine learning is not magic. Taking a large dataset and naively applying a neural network to it will not automatically give you earth-shaking insights. ML is built on top of sound and familiar mathematical principles, such as probability, statistics, linear algebra, and vector calculus—voodoo not included (though some readers may liken vector calculus to voodoo)!

We will be covering the following topics in this chapter:

  • An overview
  • Variable identification
  • Cleaning of data
  • Transformation
  • Types of analysis
  • Missing values treatment
  • Outlier treatment
Previous Section
End of Section 1
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