Recommender systems complete course beginner to advanced
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 mech...
| Corporate Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
[Place of publication not identified]
Packt Publishing
2023
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| Edition: | [First edition] |
| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
| Summary: | 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 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. 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. Basic Python knowledge is required. About The Author AI Sciences: AI Sciences is a group of experts, PhDs, and practitioners of AI, ML, computer science, and statistics. Some of the experts work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. They have produced a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of machine learning, statistics, artificial intelligence, and data science. Initially, their objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory. Today, they also publish more complete courses for a wider audience. Their courses have had phenomenal success and have helped more than 100,000 students master AI and data science |
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| Item Description: | "Published in February 2023." |
| Physical Description: | 1 video file (8 hr., 15 min.) sound, color |
| ISBN: | 9781837632039 |