intermediate Python • basics of machine learning • basics of scikit-learn
skills learned
load and analyze a dataset • train a machine learning model • evaluate a machine learning model • use a trained ML model on a website • collaborative filtering
Kim Falk
4 weeks · 4-5 hours per week · INTERMEDIATE
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.
Manning author Kim Falk shares what he likes about the Manning liveProject platform.
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project author
Kim Falk
Kim Falk is an experienced data scientist who works daily with machine learning and recommender systems. Kim is the author of Practical Recommender Systems.
prerequisites
This liveProject is for intermediate Python programmers. To begin this liveProject, you will need to be familiar with:
TOOLS
Basics of NumPy and pandas
Basics of scikit-learn
Basics of Matplotlib
Basics of Jupyter Notebook
TECHNIQUES
Basics of data science
Basics of machine learning
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