Basic Algorithms of Recommender Systems in Python
Maximise immersion with key metrics and algorithms to build a Simple/Reliable Recommendation Engine
Description
Welcome to the exhilarating journey of "Basic Algorithms of Recommender Systems in Python." This course is your backstage pass to understanding recommendation systems from the ground up. In Section 1, you'll dive headfirst into the recommendation scene, decoding implicit and explicit feedback, and tackling the pivotal challenges that drive innovation. Our dynamic trio of lectures unravels the metrics for success: from the intriguing world of HitRate that measures engagement, to the precision that ensures on-point recommendations.
Section 2 gears you up to craft your recommendation wizardry, starting with the art of ranking metrics, and unveiling the simplest recommendation engine algorithms that wield powerful results. Collaborative filtering takes you deeper into understanding user preferences, unlocking the secret sauce of personalized suggestions.
The final chapter, Section 3, catapults you into the realm of complex models, unearthing the magic of matrix decomposition. You'll traverse the theoretical landscape of complex models, equipping yourself to revolutionize recommendation systems.
Join me on this electrifying journey to master the art of recommendations, transforming how users explore content, products, and experiences in the ever-evolving digital cosmos. Your path to recommendation prowess starts here.
The course provides both the theory to understand the principles and ready-made working code that you can use in your projects.
What You Will Learn!
- Ranking metrics: NDCG, AP@k, AUC@k, DCG, IDCG
- How to work with Heuristic Algorithms
- Collaborative Algorithms and Matrix Decompositions
- Quality metrics: Precision@k, Precision, MoneyPrecision@k, Recall, HitRate
Who Should Attend!
- Anyone who needs to develop a recommender system and evaluate its quality.
- Systems Architect