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Building Recommendation Engine using Python

  /    /  Building Recommendation Engine using Python

Building Recommendation Engine using Python

Categories:
Machine Learning
Reviews:

Course Overview:

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.

Purpose:

In this class we will study and implement recommendation engines. The purpose is to understand, design, implement, and evaluate various recommendation engines.

Productivity Objectives:  

Upon completion of this course, you should be able to:

    • Understand foundational concepts for Recommendation Engine
    • Obtain hands-on experience with Recommendation Engine

This program is designed for:

    • Software Developer
    • Data Scientists
    • Data Engineers
    • A.I. Practitioners
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

Participants should have knowledge level equivalent to what is specified in “Data and Machine Learning Fundamentals” course (Beginner level course in “Machine Learning and Artificial Intelligence” learning path)

Course Information

Duration

2 Days

Mode of Delivery

Instructor led/Virtual

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