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Building Data-Driven Applications with Danfo.js

You're reading from   Building Data-Driven Applications with Danfo.js A practical guide to data analysis and machine learning using JavaScript

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801070850
Length 476 pages
Edition 1st Edition
Languages
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Authors (2):
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Stephen Oni Stephen Oni
Author Profile Icon Stephen Oni
Stephen Oni
Rising Odegua Rising Odegua
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Rising Odegua
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics
2. Chapter 1: An Overview of Modern JavaScript FREE CHAPTER 3. Section 2: Data Analysis and Manipulation with Danfo.js and Dnotebook
4. Chapter 2: Dnotebook - An Interactive Computing Environment for JavaScript 5. Chapter 3: Getting Started with Danfo.js 6. Chapter 4: Data Analysis, Wrangling, and Transformation 7. Chapter 5: Data Visualization with Plotly.js 8. Chapter 6: Data Visualization with Danfo.js 9. Chapter 7: Data Aggregation and Group Operations 10. Section 3: Building Data-Driven Applications
11. Chapter 8: Creating a No-Code Data Analysis/Handling System 12. Chapter 9: Basics of Machine Learning 13. Chapter 10: Introduction to TensorFlow.js 14. Chapter 11: Building a Recommendation System with Danfo.js and TensorFlow.js 15. Chapter 12: Building a Twitter Analysis Dashboard 16. Chapter 13: Appendix: Essential JavaScript Concepts 17. Other Books You May Enjoy

Introduction to machine learning

In this section, we will introduce ML by using a simple analogy that might serve as common ground to establish our explanation. We will also see why and how ML works.

We will start the section by using an information transfer system as a simple analogy for ML.

A simple analogy of a machine learning system

I remember a time I was in a Twitter Space involving a discussion about ML and some other cool topics. I was told to give a brief introduction to ML for those who were interested but didn't fully get the gist.

The majority of people in this Twitter Space were software engineers with no previous knowledge of math, statistics, or any topic related to ML, and I came across instances where people failed to understand the terminology of the topic due to the addition of some technical terms.

This section aims to explain ML by avoiding too many technical terms and finding a common ground through which ML can be explained.

Using an information...

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