Machine Learning Make Your Own Recommender System

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Machine Learning: Make Your Own Recommender System

Machine Learning: Make Your Own Recommender System
Author :
Publisher : Machine Learning for Beginners
Total Pages : 120
Release :
ISBN-10 : 1726769038
ISBN-13 : 9781726769037
Rating : 4/5 (037 Downloads)

Book Synopsis Machine Learning: Make Your Own Recommender System by : Oliver Theobald

Download or read book Machine Learning: Make Your Own Recommender System written by Oliver Theobald and published by Machine Learning for Beginners. This book was released on 2018-10-06 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn How to Make Your Own Recommender System in an Afternoon.Recommender systems are one of the most visible applications of machine learning and data mining today and their uncanny ability to convert our unspoken actions into items we desire is both addicting and concerning. And whether recommender systems excite or scare you, the best way to manage their influence and impact is to understand the architecture and algorithms that play on your personal data. Recommender systems are here to stay and for anyone beginning their journey in data science, this is a lucrative space for future employment.This book will get you up and running with the basics as well as the steps to coding your own recommender system. Exercises include predicting book recommendations, relevant house properties for online marketing purposes, and whether a user will click on an ad campaign. The contents of this book is designed for beginners with some background knowledge of data science, including classical statistics and computing programming. If this is your first exposure to data science, you may want to spend a few hours to read my first book Machine Learning for Absolute Beginners before you get started here.Topics covered in this book: Setting Up A Sandbox Environment With Jupyter NotebookWorking With DataData ReductionBuilding a Collaborative Filtering ModelBuilding a Content-Based Filtering ModelEvaluationPrivacy & EthicsFuture of Recommender SystemsPlease feel welcome to join this introductory course by buying a copy or sending a free sample to your preferred device.


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