Recommender Systems with Machine Learning - Content-Based Filtering-2

This video tutorial covers the development of a machine learning-based content recommendation system. It outlines the key steps, including data preparation using libraries like Pandas and Numpy, extracting data insights, implementing TFIDF for content filtering, and building a recommendation engine with Python libraries such as SK and Fuzzy. The tutorial concludes with testing the recommender system.

Describe the process of building a recommendation engine using Python.

What steps are involved in testing a recommender system?