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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

Skills you'll learn:

Similarity ScorePrincipal component analysis

Prerequisites:

scikit-learnCRISP-DMBasic calculusMatrix operationsNumPy

Advanced

4 weeks

Last Updated January 7, 2025

Skills you'll learn:

Similarity Score • Principal component analysis • Dimensionality reduction • Types of recommenders

Prerequisites:

scikit-learn • CRISP-DM • Basic calculus

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

Photo of David Elliott

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.

Photo of Matt Maybeno

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.

Photo of Joshua Bernhard

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.

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