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Building Practical Recommendation Engines – Part 1

Introduction to recommendation engines

The Course Overview
Recommendation engine definition
Types of recommender systems
Evolution of recommender systems with technology

Building your first recommendation engine

Loading and formatting data
Calculating similarity between users
Predicting the unknown ratings for users

Recommendation engines explained

Nearest neighborhood-based recommendation engines
Content-based recommender system
Context-aware recommender system
Hybrid recommender systems
Model-based recommender systems

Convolutional neural networks

Neighborhood-based techniques
Mathematical model techniques
Machine learning techniques
Classification models
Clustering techniques and dimensionality reduction.
Vector space models
Evaluation techniques

Building Collaborative Filtering Recommendation Engines

Installing the recommenderlab package in RStudio
Datasets available in the recommenderlab package
Exploring the dataset andbuilding user-based collaborative filtering
Building an item-based recommender model
Collaborative filtering using Python
Data exploration
User-based collaborative filtering with the k-nearest neighbors
Item-based recommendations

The Course Overview

This video will brief introduction about the entire course

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