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Recommender Systems with Machine Learning
Recommender Systems with Machine Learning

Recommender Systems with Machine Learning: Build Recommender Systems for Real-World Applications Using Machine Learning

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zł39.99 zł443.99
Video Mar 2023 6hrs 17mins 1st Edition
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zł39.99 zł443.99
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Profile Icon AI Sciences
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zł39.99 zł443.99
Video Mar 2023 6hrs 17mins 1st Edition
Video
zł39.99 zł443.99
Subscription
Free Trial
Video
zł39.99 zł443.99
Subscription
Free Trial

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Key benefits

  • Build recommender systems using ML from the perspective of content-based and collaborative filtering
  • Implementation of ML with data analysis on real-world datasets of movies and Spotify songs
  • Learn to program with Python and how to use ML concepts to develop recommender systems

Description

Have you ever thought how YouTube adjusts your feed as per your favorite content? Ever wondered! Why is your Netflix recommending your favorite TV shows? Have you ever wanted to build a customized recommender system for yourself? Then this is the course you are looking for. We will begin with the theoretical concepts and fundamental knowledge of recommender systems. You will gain an understanding of the essential taxonomies that form the foundation of these systems. You will be learning how to use the power of Python to evaluate your recommender systems datasets based on user ratings, user choices, music genres, categories of movies, and their year of release. A practical approach will be adopted to build content-based filtering and collaborative filtering techniques for recommender systems. Moving ahead, you will learn all the basic and necessary concepts for the applied recommender systems models along with the machine learning models. Moreover, various projects have been included in this course to develop a very useful experience for you. By the end of this course, you will be able to relate the concepts and theories for recommender systems in various domains, implement machine learning models for building real-world recommendation systems, and evaluate the machine learning models. All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Recommender-Systems-with-Machine-Learning

Who is this book for?

No prior knowledge of recommender systems, machine learning, data analysis, or mathematics is needed. Only the working knowledge of basics of Python is required. You will start from the basics and gradually build your knowledge in the subject. This course is designed for both beginners with some programming experience and even those who know nothing about data analysis, ML, and RNNs. The course is suitable for individuals who want to advance their skills in ML, master the relation of data analysis with ML, build customized recommender systems for their applications, and implement ML algorithms for recommender systems.

What you will learn

  • Explore AI-integrated recommender systems basics
  • Look at the basic taxonomy of recommender systems
  • Study the impact of overfitting, underfitting, bias, and variance
  • Build content-based recommender systems with ML and Python
  • Build item-based recommender systems using ML techniques and Python
  • Learn to model KNN-based recommender engine for applications

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 13, 2023
Length: 6hrs 17mins
Edition : 1st
Language : English
ISBN-13 : 9781837631667
Category :
Languages :
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Product Details

Publication date : Mar 13, 2023
Length: 6hrs 17mins
Edition : 1st
Language : English
ISBN-13 : 9781837631667
Category :
Languages :
Tools :

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Table of Contents

6 Chapters
Introduction Chevron down icon Chevron up icon
Motivation for Recommender System Chevron down icon Chevron up icon
Basic of Recommender Systems Chevron down icon Chevron up icon
Machine Learning for Recommender System Chevron down icon Chevron up icon
Project 1: Song Recommendation System Using Content-Based Filtering Chevron down icon Chevron up icon
Project 2: Movie Recommendation System Using Collaborative Filtering Chevron down icon Chevron up icon
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