Recommender Systems and Personalization Virtual Internship
In this virtual internship, students will gain hands-on experience in designing and implementing recommender systems that leverage machine learning and data mining techniques to provide personalized product, content, or service recommendations to users. They will learn how to collect and preprocess data, build and evaluate different types of recommender models, and deploy these systems to real-world applications. Upon completion, students will be equipped with the skills to pursue careers in data science, personalization, and recommendation engine development.
Track Overview
Tasks & Milestones
Exploratory Data Analysis for Recommender Systems
IntermediateIn this task, students will perform exploratory data analysis on a dataset relevant to recommender systems, such as user-item interactions or product metadata.
Implementing a User-Based Collaborative Filtering Recommender
IntermediateIn this task, students will implement a user-based collaborative filtering recommender system and evaluate its performance.
Implementing a Content-Based Recommender for Movies
IntermediateIn this task, students will build a content-based recommender system for movie recommendations using movie metadata.
Implementing a Hybrid Recommender System for E-commerce
IntermediateIn this task, students will build a hybrid recommender system for an e-commerce platform that combines collaborative and content-based filtering.
Prerequisites
- • Proficiency in Python programming
- • Basic understanding of machine learning concepts
- • Experience with data manipulation and analysis using tools like Pandas and NumPy
Certificate
Certificate of Completion
Earn a certificate upon successful completion