Basic Algorithms of Recommender Systems in Python

Basic Algorithms of Recommender Systems in Python, available at $19.99, with 8 lectures.

You will learn about 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 This course is ideal for individuals who are Anyone who needs to develop a recommender system and evaluate its quality. or Systems Architect It is particularly useful for Anyone who needs to develop a recommender system and evaluate its quality. or Systems Architect.

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Summary

Title: Basic Algorithms of Recommender Systems in Python

Price: $19.99

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: $19.99

Quality Status: approved

Status: Live

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

Target Audiences

Anyone who needs to develop a recommender system and evaluate its quality.
Systems Architect

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.

Course Curriculum

Chapter 1: Introduction. Metrics for assessing the quality of recommender systems.

Lecture 1: Introduction: implicit and explicit feedback, key problems

Lecture 2: HitRate

Lecture 3: Precision

Chapter 2: Your first simple recommendation system with ranking metrics.

Lecture 1: Order of recommendations; ranking metrics

Lecture 2: The simplest recommendation engine algorithms

Lecture 3: Collaborative filtering

Chapter 3: Lesson 3

Lecture 1: Matrix Decomposition

Lecture 2: Theory: complex models

Instructors

Max Tcvetkov
Product Design, ex: "Kaspersky"

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Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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