Unsupervised Machine Learning and Recommendation Systems

Explore unsupervised learning in data science, focusing on clustering techniques, k-means algorithms, metrics, and applications for enhanced data analysis.

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.

Learn about the different methods used to create recommendation engines with Python.

Learn how to evaluate unsupervised machine learning models, including clustering, dimensionality reduction, and recommendation systems, to improve their performance.