Recommender Systems

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Search Engines for Web and Enterprise Data (Coursera)

This course introduces the technologies behind web and search engines, including document indexing, searching and ranking. You will also learn different performance metrics for evaluating search quality, methods for understanding user intent and document semantics, and advanced applications including recommendation systems and summarization. Real-life examples and case studies are [...]

Recommender Systems (Coursera)

In this course you will: a) understand the basic concept of recommender systems; b) understand the Collaborative Filtering; c) understand the Recommender System with Deep Learning; d) understand the Further Issues of Recommender Systems. Please make sure that you’re comfortable programming in Python and have a basic knowledge of [...]

Basic Recommender Systems (Coursera)

This course introduces you to the leading approaches in recommender systems. The techniques described touch both collaborative and content-based approaches and include the most important algorithms used to provide recommendations. You'll learn how they work, how to use and how to evaluate them, pointing out benefits and limits of [...]

Recommender Systems: Evaluation and Metrics (Coursera)

In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity. You will learn how different metrics relate to [...]

Introduction to Recommender Systems: Non-Personalized and Content-Based (Coursera)

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. [...]

Text Retrieval and Search Engines (Coursera)

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather [...]

Unsupervised Learning, Recommenders, Reinforcement Learning (Coursera)

May 20th 2024
Unsupervised Learning, Recommenders, Reinforcement Learning (Coursera)
Course Auditing
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In the third course of the Machine Learning Specialization, you will: Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection; Build recommender systems with a collaborative filtering approach and a content-based deep learning method; Build a deep reinforcement learning [...]

Advanced Recommender Systems (Coursera)

May 20th 2024
Advanced Recommender Systems (Coursera)
Course Auditing
Categories
Effort
Languages
In this course, you will see how to use advanced machine learning techniques in order to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need [...]

Machine Learning with Python (Coursera)

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will [...]

Matrix Factorization and Advanced Techniques (Coursera)

In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. [...]