Mastering Personalized Recommendations: From Data Science to Deployment with Python
What you will learn
Understand the Basics of Machine Learning: Learn the fundamental concepts of machine learning and how they apply to building recommendation systems.
Master Data Manipulation Using Python: Gain proficiency in using Python and the pandas library to import, clean, and manipulate large datasets effectively.
Apply Text Processing Techniques: Learn to use techniques such as CountVectorizer and TF-IDF for processing textual data to enhance the quality of recommendatio
Implement Machine Learning Algorithms: Utilize scikit-learn to implement algorithms that enable personalized movie recommendations based on user preferences.
Design and Build a User Interface: Create an intuitive user interface using Streamlit, making the system accessible and easy to use for end-users.
Evaluate and Interpret Model Performance: Understand how to evaluate the effectiveness of recommendation models using metrics like cosine similarity.
Serialize and Deploy Machine Learning Models: Learn how to use serialization with pickle to save and load trained models, enabling their use in real-world appli
Incorporate Feedback and Improve the System: Understand methods for incorporating user feedback into the recommendation system
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