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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.

This project-based course equips learners with the skills to design, develop, and implement a personalized book recommendation system using Python. Spanning two core modules, the course introduces foundational concepts of collaborative and content-based filtering and builds toward a functional hybrid model. Learners will begin by analyzing user data, constructing user-item interaction matrices, and evaluating baseline models. They will then apply advanced data handling techniques using libraries like Pandas and NumPy, and integrate multiple recommendation strategies into a single hybrid engine.

Through practical lessons, coding exercises, and quizzes, learners will progressively apply machine learning logic, synthesize similarity computations, and construct real-world recommendation systems that combine user behavior with item features. By the end of the course, learners will be able to confidently build scalable recommendation pipelines tailored for dynamic, user-centric applications.

This course empowers learners to design, develop, and evaluate movie recommendation systems using real-world data and Python programming. Tailored for data enthusiasts and aspiring machine learning developers, the course introduces the practical applications of recommender systems across modern digital platforms such as Netflix, Amazon, and YouTube.

Beginning with foundational concepts, learners will set up their Python environment and build a simple recommender based on popularity metrics. As the course progresses, learners will transition to constructing a more nuanced content-based recommender, utilizing rich metadata such as genres, keywords, and cast to provide personalized recommendations. By completing this course, learners will gain hands-on experience in preprocessing data, engineering features, and applying core machine learning techniques for real-time decision-making. The instruction is aligned with Bloom鈥檚 Taxonomy, guiding learners to construct, analyze, and apply recommender models effectively.