Building Recommendation Engine using Python

Learn how to Build your recommendation engine with the help of Python, from basic models to content-based and collaborative filtering

Duration

2 Days

Level

Intermediate Level

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Currently, there are millions of products on Amazon. We don’t even know which product to buy but Amazon knows this through it’s Recommendation engine.

This course focuses on building Recommendation Engine using Python programming language. The course provides an introduction to Recommendation engine, ways to build it using various options like neighbourhood based, model based, content based and context aware recommendation engines. It also compares these approaches from various perspectives.

After Completing this course students will be able to build Real world Recommendation Engines.

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Software Developer
Data Scientists
Data Engineers
A.I. Practitioners

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Introduction to Recommender Systems

Overview
History behind Recommender Systems
Predictions vs Recommendations
Future of Recommender Systems
Setting up the Development Environment

Recommender Systems Theory

Types of Recommender Systems
Non-Personalized and Stereotype-Based Recommenders
Introduction to Content-Based Recommenders
TF-IDF and Content Filtering
Content-Based Filtering
Entree Style Recommenders
Case-Based Reasoning
Dialog-Based Recommenders
Search, Recommendation, and Target Audiences
Beyond TF-IDF

Algorithm for Recommender System

Item-based collaborative filtering
Non-negative matrix factorization
Contenting-based filtering
kNN
Knowledge-based Recommender systems
Clustering
Vector similarity measures: Pearson, Jaccard, cosine

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Basic Knowledge of Python Programing

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Building Recommendation Engine using Python

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