2022-08-31-clustering.md

DIY Clustering algorithms and models

• Goal: learn about different types of clustering algorithms and use them in datasets in notebooks.
• Dates: from 31 August to 6 September
• Where: #project-of-the-week in DataTalks.Club (get in slack here: https://datatalks.club/slack.html)

For more information about the "Project of the Week" initiative at DataTalks.Club, see README.md.

If you want to receive reminders about this event, sign up here

Technologies

• Scikit-Learn
• Jupyter notebooks

Note: this is a suggested list of technologies, you can chose alternatives instead

Plan

This is a proposed plan only, you don’t have to follow it day-by-day

Day 1 (31 August, Wednesday)

• Come up with a project idea
• Select the dataset for your project
• Create a github repository
• Share your progress in Slack and in social media

Day 2 (1 September, Thursday)

• Learn about K-means clustering
• Push your changes to github
• Share your progress in Slack and in social media

Suggested materials:

• 📺 StatQuest: K-means clustering
• 📺 K means Clustering Implementation | Machine Learning Project 2

Found good materials? Create a PR with links!

Day 3 (2 September, Friday)

• Learn about Mean-shift Clustering
• Push your changes to github
• Share your progress in Slack and in social media

Suggested materials:

• 📺 StatQuest: Hierarchical Clustering
• 📺 Unsupervised Machine Learning - Hierarchical Clustering with Mean Shift Scikit-learn and Python

Found good materials? Create a PR with links!

Day 4 (3 September, Saturday)

• Learn about Agglomerative Hierarchical Clustering
• Push your changes to github
• Share your progress in Slack and in social media

Suggested materials:

• 📺 Hierarchical Clustering | Agglomerative and Divisive Hierarchical Clustering Explained | Edureka
• 📺 Agglomerative Clustering: how it works
• 📺 How to Perform Hierarchical Clustering in Python (Step by Step)

Found good materials? Create a PR with links!

Day 5 (4 September, Sunday)

• Learn about DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
• Push your changes to github
• Share your progress in Slack and in social media

Suggested materials:

• 📺 Clustering with DBSCAN, Clearly Explained!!!
• 📺 DBSCAN Clustering Easily Explained with Implementation
• 📺 DBSCAN Algorithm In Python | DBSCAN clustering Algorithm example | Density based clustering python

Found good materials? Create a PR with links!

Day 6 (5 September, Monday)

• Learn about Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)

Suggested materials:

• 📺 Clustering (4): Gaussian Mixture Models and EM
• 📺 EM Algorithm In Machine Learning | Expectation-Maximization | Machine Learning Tutorial | Edureka

Found good materials? Create a PR with links!

Day 7 (6 September, Tuesday)

• Continue exploring more about this topic
• Polish the documentation for your project
• Push your changes to github
• Share your progress in Slack and in social media
• Give us feedback
• Add the link to your project to this project of the week github page

Materials

• 🏫 Advanced ML course from Google: Clustering
• 🏫 Cluster Analysis in Data Mining (Coursera)
• 🗒️ The 5 Clustering Algorithms Data Scientists Need to Know
• 🗒️ Awesome Clustering Resources
• 🗒️ Clustering — When You Should Use it and Avoid It
• 🗒️ How to Answer Business Questions Using Cluster Analysis

Notebooks:

• 💻 Notebooks on clustering and dimensionality reduction techniques
• 💻 Python Clustering Exercises

Datasets

• 💾 Clustering datasets on Kaggle
• 💾 UCI Machine Learning Repository: Clustering
• 💾 Clustering basic benchmark

Note: If you know other good resources about clustering, send a PR

Legend

• 🏫 Course
• 💾 Dataset
• 🗒️ Article
• 📺 Video tutorial
• 💻 Code

Projects

List of projects from our participants:

• Customer Segmentation by Esteban Encina
• Project link 2
• ...
• (Create a PR)