Skills you'll learn:

Unsupervised Machine Learning and Recommendation Systems
Course
This course covers advanced data science topics related to unsupervised machine learning. You will apply techniques including clustering and dimensionality reduction, then learn about the different strategies for generating personalized recommendations. The final lesson of the course covers practical advice for data scientists using unsupervised models in the real world, including how to test them both "online" and "offline". For the final project, you will build a hybrid recommendation system for data science articles.
This course covers advanced data science topics related to unsupervised machine learning. You will apply techniques including clustering and dimensionality reduction, then learn about the different strategies for generating personalized recommendations. The final lesson of the course covers practical advice for data scientists using unsupervised models in the real world, including how to test them both "online" and "offline". For the final project, you will build a hybrid recommendation system for data science articles.
Advanced
4 weeks
Last Updated January 7, 2025
Prerequisites:
Advanced
4 weeks
Last Updated January 7, 2025
Skills you'll learn:
Prerequisites:
Course Lessons
Lesson 1
Clustering
Explore unsupervised learning in data science, focusing on clustering techniques, k-means algorithms, metrics, and applications for enhanced data analysis.
Lesson 2
Dimensionality Reduction
Learn the fundamentals of dimensionality reduction and how to use it in your models effectively. Utilize Scikit-learn and matplotlib to calculate and visualize the dimension reduction process.
Lesson 3
Recommendation Systems
Learn about the different methods used to create recommendation engines with Python.
Lesson 4
Unsupervised Machine Learning Model Evaluation
Learn how to evaluate unsupervised machine learning models, including clustering, dimensionality reduction, and recommendation systems, to improve their performance.
Lesson 5 • Project
Project: Recommendation System
Implement a system for recommending technical articles from the IBM Watson platform, using ranking-based, content-based, and collaborative filtering approaches
Taught By The Best

David Elliott
Data Scientist, Data Engineer
David Elliott is both a data scientist and a data engineer at a small data management company. He has extensive experience in education, both as an instructor and as a curriculum developer.

Matt Maybeno
Principal Software Engineer
Matt is a Principal Software Engineer at SOCi. With a masters in Bioinformatics from SDSU, he utilizes his cross domain expertise to build solutions in NLP and predictive analytics.

Joshua Bernhard
Staff Data Scientist, Marketplace
Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.
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About Unsupervised Machine Learning and Recommendation Systems
Take Udacity's Unsupervised Machine Learning and Recommendation Systems course and learn how to run statistically valid tests, interpret results and generate personalized recommendations based on user data.