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