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Build an ML Recommender System eBook for free
Build an ML Recommender System eBook for free
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Build an ML Recommender System eBook for free
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.
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.
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
- 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.
