Recommender systems are one of the most popular and lucrative uses of machine learning, allowing businesses and organizations to give personalized suggestions to their customers.
In this liveProject, you’ll use common tools of the Python data ecosystem to design, build, and evaluate a movie recommendation model for the movie website. You’ll kick off your project by building simple genre charts, then work with existing movie rating data to implement personalized recommendations for each customer. When you’ve completed this hands-on and interesting project, you’ll have mastered a cornerstone technique of machine learning that’s in demand across companies and industries.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
This liveProject is for intermediate Python programmers. To begin this liveProject, you will need to be familiar with:
- Basics of NumPy and pandas
- Basics of scikit-learn
- Basics of Matplotlib
- Basics of Jupyter Notebook
- Basics of data science
- Basics of machine learning
you will learn
In this liveProject, you’ll learn to put common Python data science libraries into action to build an in-demand machine learning model.
- Data manipulation and analysis using pandas
- Collaborative filtering with negative matrix factorization implemented using scikit-learn
- Produce personalized recommendations using latent vectors created by factorization
- Visualizing the data in the reports using Matplotlib and Seaborn
- Evaluate and optimize algorithm hyperparameters