This hands-on project-based course guides learners through the process of designing, developing, and evaluating a functional Book Recommendation Engine using Python and data science techniques. Beginning with foundational principles, learners will identify key components of recommender systems, prepare structured datasets, and apply user-driven filters to generate personalized recommendations.
In the advanced stages, learners will construct content-based filtering models using textual data, extract meaningful features with TF-IDF and Count Vectorizers, and compute similarity scores to rank items effectively. Throughout the course, learners will also integrate, combine, and transform multi-attribute metadata (e.g., author, title, genre) to enhance the relevance of outputs. By the end of this course, learners will be able to design, implement, and refine a real-world recommendation engine that simulates industry-standard systems.
