Building a Recommendation Engine Using Python

Currently, there are millions of products on Amazon. How does anyone know which product to buy — Amazon makes suggestions through their Recommendation Engine.

The Building a Recommendation Engine Using Python course focuses on building a Recommendation Engine using Python programming language. On the first day, students will receive an introduction to Recommendation Engines, ways to build them using various options like neighborhood-based, model-based, content-based and context-aware Recommendation Engines. Then, the course explores a comparison of these approaches from various perspectives as well as hands-on creation and utilization of different algorithms. After completing this course students will be able to build real-world Recommendation Engines.

Course Summary

Understand, design, implement and evaluate various recommendation engines.
Developers and developer teams needing to learn to build recommendation engines using Python.
Skill Level: 
Learning Style: 

Workshops are instructor-led lab-intensives focused on the practical application of technologies through the facilitation of a project-related lab. Workshops are just the opposite of Seminars. They deliver the highest level of knowledge transfer of any format. Think wide (breadth) and deep (depth).

Workshop help
2 Days
Productivity Objectives: 
  • Discuss foundational concepts for Recommendation Engines
  • Design, implement and evaluate a Recommendation Engine

What You'll Learn

In the Building a Recommendation Engine Using Python training course you’ll learn:

  • Introduction to Recommender Systems
    • Overview
    • The 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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