Unsupervised Learning examples including Clustering and Collaborative Filtering

There are 7 examples of Notebooks to learn the basics related to Clustering and Collaborative Filtering:

Clustering example with KMeans and Agglomerative approach using the Iris dataset
Coding from scratch - Implementing the KMeans algorithm and analysis using different data shapes
Coding from scratch - Implementing the DBScan algorithm and analysis using different data shapes
Introduction to Collaborative filtering, specifically to item-based CF
Item-based implementation using Cosine similarity from KNN algorithm and Rating prediction equation
Implementation of a Matrix Factorization approach based on learning / optimization to recommend movies
Implement a recommender system based on Surprise package

Some examples of Customer Segmentation using unsupervised learning:

Some examples of Movie Recommentation using Collaborative Filtering:

Name		Name	Last commit message	Last commit date
Latest commit History 9 Commits
data		data
examples		examples
ml-1m		ml-1m
.DS_Store		.DS_Store
.gitignore		.gitignore
1_clustering.ipynb		1_clustering.ipynb
2_k-means.ipynb		2_k-means.ipynb
3_dbscan.ipynb		3_dbscan.ipynb
4_intro_cf.ipynb		4_intro_cf.ipynb
5_item-based-cf.ipynb		5_item-based-cf.ipynb
6_matrix factorization code.ipynb		6_matrix factorization code.ipynb
7_matrix factorization.ipynb		7_matrix factorization.ipynb
8_time-series-clustering.ipynb		8_time-series-clustering.ipynb
README.md		README.md
movielens-full.ipynb		movielens-full.ipynb
movielens-item-based-cf.ipynb		movielens-item-based-cf.ipynb
requirements.txt		requirements.txt

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