Hands-On Recommendation Systems with Python

Hands-On Recommendation Systems with Python
Author: Rounak Banik
Publisher: Packt Publishing Ltd
Total Pages: 141
Release: 2018-07-31
Genre: Computers
ISBN: 1788992539


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With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.


Hands-On Recommendation Systems with Python
Language: en
Pages: 141
Authors: Rounak Banik
Categories: Computers
Type: BOOK - Published: 2018-07-31 - Publisher: Packt Publishing Ltd

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With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collabor
Hands-On Recommendation Systems with Python
Language: en
Pages: 146
Authors: Rounak Banik
Categories: Electronic books
Type: BOOK - Published: 2018-07-27 - Publisher:

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With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collabor
Approaching (Almost) Any Machine Learning Problem
Language: en
Pages: 300
Authors: Abhishek Thakur
Categories: Computers
Type: BOOK - Published: 2020-07-04 - Publisher: Abhishek Thakur

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This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is n
Building Recommender Systems with Machine Learning and AI.
Language: en
Pages:
Authors: Frank Kane
Categories:
Type: BOOK - Published: 2018 - Publisher:

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Automated recommendations are everywhere: Netflix, Amazon, YouTube, and more. Recommender systems learn about your unique interests and show the products or con
Statistical Methods for Recommender Systems
Language: en
Pages: 317
Authors: Deepak K. Agarwal
Categories: Computers
Type: BOOK - Published: 2016-02-24 - Publisher: Cambridge University Press

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Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is