Book Image

Julia for Data Science

By : Anshul Joshi
2 (1)
Book Image

Julia for Data Science

2 (1)
By: Anshul Joshi

Overview of this book

Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.
Table of Contents (17 chapters)
Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Content-based filtering


Content-based filtering creates a profile of the user and uses this profile to give relevant recommendations to the user. The profile of the user is created by the history of the user.

For example, an e-commerce company can track the following details of the user to generate recommendations:

  • Items ordered in the past

  • Items viewed or added to the cart but not purchased

  • User browsing history to identify what kinds of products the user may be interested in

The user may or may not have manually given ratings to these items, but various factors can be considered to evaluate their relevance to the user. Based on this, new items are recommended to the user that would be interesting to that user.

The process as shown, takes the attributes from the user profile and matches them with the attributes of the items available. When there are relevant items available, these are considered to be of interest to the user and are recommended.

Therefore, the recommendations are heavily dependent...