Unsupervised Learning: Clustering Techniques | K-Means, DBSCAN| Day 14/30 of Data Science in 30 Days

Описание: Welcome to Day 14 of our “Data Science in 30 Days” full course series!
In this session, we dive deep into one of the most exciting parts of Machine Learning — Unsupervised Learning.
You’ll understand how algorithms group data without labeled outputs, focusing on clustering techniques such as K-Means, Hierarchical Clustering, and DBSCAN.

This video includes:

📘 Concept explanation of unsupervised learning

💡 Real-world examples of clustering

🧠 Step-by-step implementation of K-Means, Hierarchical & DBSCAN using the Iris dataset

📊 Visual representation of clusters using Python and scikit-learn

💬 Interpretation of clustering results

Whether you’re preparing for a Data Science interview or working on your next ML project, this video will solidify your understanding of clustering techniques.

🧰 Resources & Links:

📂 Code Notebook:
👉 Google Colab Notebook : https://colab.research.google.com
(Upload the notebook provided in this tutorial)

📚 Official Documentation:

🔹Scikit-learn Clustering Overview : https://scikit-learn.org/stable/modul...

🔹K-Means Clustering : https://scikit-learn.org/stable/modul...

🔹DBSCAN Algorithm : https://scikit-learn.org/stable/modul...

🔹Agglomerative Hierarchical Clustering : https://scikit-learn.org/stable/modul...

🧩 Additional Learning:

Day 11: Introduction to Machine Learning :    • Machine Learning Basics | Day 11/30 of Dat...  

Day 12: Supervised Learning – Regression Models :    • Supervised Learning – Regression Models | ...  

Day 13: Model Selection & Evaluation :    • Model Selection & Evaluation | Day 13/30 o...  

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OUTLINE:
00:00:00 : A Gentle Introduction to Clustering
00:00:39 : Unsupervised Learning Basics and Use Cases
00:01:44 : Our Playground for Today (Iris + PCA + Setup)
00:02:38 : Getting Data in Python and PCA Transform
00:04:06 : Finding the Center of Gravity (K-Means)
00:05:11 : K-Means Iteration, Code, and Visualization
00:07:07 : The Power and Pitfalls of K-Means
00:08:00 : K-Means Failure Modes and Popular Applications
00:09:09 : Building a Family Tree of Data (Hierarchical)
00:11:13 : Hierarchical in Code and Visualization
00:12:09 : Strengths and Weaknesses of Hierarchical
00:13:13 : Hierarchical Trade-offs and Best Uses
00:14:12 : Greediness Caveat and Domains for Hierarchical
00:15:16 : Clustering by Density (DBSCAN)
00:16:14 : DBSCAN Core/Border/Noise and Code
00:17:07 : DBSCAN in scikit-learn and Labels
00:17:50 : Advantages and Challenges of DBSCAN
00:19:05 : DBSCAN Challenges: Sensitivity and Mixed Densities
00:20:28 : A Final Comparison and Summary
00:21:19 : Final Guidance
00:22:18 : Practical Steps
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