Build an ML Recommender System

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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liveProject liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $49.99 self-paced learning
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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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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

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

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
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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