Recommender Systems with Machine Learning

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-Lea…

Type
video
Category
Data
publication date
2023-03-13
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

duration
377
key features
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
approach
This is a comprehensive, easy-to-understand, self-explanatory, to-the-point, and practical course with live coding and two in-depth projects covering complete course contents.

Every module has engaging content; a completely practical approach is used along with brief theoretical concepts. At the end of every module, there will be a quiz, followed by its solution in the next video.
audience
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.
meta description
A complete course on recommender systems using ML with practical content and detailed code notebooks. The course is filled with two real-world projects with quizzes to build applications from scratch using ML with Python.
short description
The course is crafted to help you understand not only the role and impact of recommender systems in real-world applications but also provide hands-on experience in developing complete recommender systems engines for your customized dataset using projects. This learning-by-doing course will help you master the concepts and methodology of Python.
subtitle
Build Recommender Systems for Real-World Applications Using Machine Learning
keywords
Recommender Systems, machine learning, Python, deep learning, RNNs, Data Analysis
Product ISBN
9781837631667