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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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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

Why machine learning works

During our illustration of training the ML model, we talked about generating a form of representation that is used to build our ML system. One thing to note is that this information or the data used to generate the form of representation is the data representation of our future source of information. Why do we need the data representation of our future source of information? In this sub-section, we will look into this and see how it helps in creating ML models.

Let's assume we are told to carry out research about product interest in a particular community. Imagine this community consists of a large number of people and we are only able to reach a few people – let's say 50% of the community population.

The idea is that, from the information obtained from 50% of the population, we should be able to generalize for the remaining 50% of the population. We assume this because a set of people from the same community or population are assumed...

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