Recommender systems are algorithms that suggest relevant items to users (movies, books, products, or a service). Recommender systems are critical in specific industries to generate massive incomes efficiently or stand out significantly from competitors.
The course begins with basic recommender system concepts. You will learn important recommender system taxonomies and recommender system mechanism development using machine and deep learning with Python. Python as a programming language will be taught in this course to implement machine and deep learning concepts efficiently. You will model a k-nearest neighbor-based recommender engine for various applications and know the pros and cons of deep learning-based mechanisms.
You will build a recommender system for apps such as Spotify and explore neural collaborative filtering and variational auto-encoders for collaborative filtering. You will explore various matrices (item context, user rating, and error). You will understand recommender system quality, online/offline evaluation techniques, dataset partitioning, and overfitting.
Upon completing the course, you will understand the roles and impacts of recommender systems in real-world applications with a unique hands-on experience in developing complete recommender system engines for customized datasets in various projects.
All resources are available at: https://github.com/PacktPublishing/Recommender-Systems-Complete-Course-…
publication date
2023-02-23
what you will learn
Explore recommender systems with integrated artificial intelligence
Build item-based recommender systems with machine learning/Python
Understand the pros and cons of deep learning in recommender systems
Learn the basic neural network models for recommendations
Understand the mechanism of generic deep learning-based approaches
Implement two-tower models for developing a recommender system
key features
This complete package explores recommender system applications and machine/deep learning with Python * Learn to implement deep learning-based recommender systems and two-tower model implementation * Explore content-based concepts for an item-based recommender system with machine learning and Python
approach
The course is designed to assist you in understanding concepts clearly, and provides a unique hands-on experience. This course is expressive and self-explanatory, to the point, and practical with live coding. This straightforward learning-by-doing approach will help you in mastering the concepts and methodologies easily. This is a complete package with in-depth projects covering all course content.
audience
This course is designed for individuals wanting to advance their applied machine/deep learning and master data analysis; individuals wishing to build customized recommender systems for their apps and implement machine/deep learning algorithms; individuals passionate about content and collaborative filtering-based and two tower-based recommender systems. Machine and deep learning practitioners, research scholars, and data scientists would also benefit from this course. As prerequisites, no prior recommender systems, ML, data analysis knowledge is needed. Basic Python knowledge is required.
short description
This comprehensive course will guide you to use the power of Python to evaluate recommender system datasets based on user ratings, user choices, music genres, categories of movies, and their years of release with a practical approach to build content-based and collaborative filtering techniques for recommender systems with hands-on experience.
subtitle
Master a recommender system that suggests items most pertinent to a specific user for data analysis
keywords
machine learning topologies, recommender systems, integrated artificial intelligence, collaborative filtering, variational autoencoders, overfitting, underfitting, bias and variance, machine learning, and deep learning
Product ISBN
9781837632039
