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Free Download Basic Algorithms of Recommender Systems in Python
Published 8/2023
Created by Max Tcvetkov
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 8 Lectures ( 37m ) | Size: 308 MB
Maximise immersion with key metrics and algorithms to build a Simple/Reliable Recommendation Engine
What you'll 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
Requirements
A basic knowledge to Python. No experience with ML is required.
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.
Who this course is for
Anyone who needs to develop a recommender system and evaluate its quality.
Systems Architect
Homepage
Code:
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