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prerequisites
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
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Look inside
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.

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

features

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Project roadmap
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Get Help
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Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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