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

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

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...   ------------------------------------------------------------------------------------------------------------------------ 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 ------------------------------------------------------------------------------------------------------------------------ #dbscan #kmeans #clustering #unsupervised #unsupervisedlearning #datasciencein30days #hierarchical #ai #aivideo #aieducation #ml #mltutorial #subscribe #foryou #python #pythontutorial #pythonprogramming #chatgpt #mlalgorithms #scikit #scikitlearn #datascience #dataanalytics #machinelearning #bigdata #deeplearning #artificialintelligence #ai #datavisualization #thedatakey #datasciencewiththedatakey #learnwiththedatakey #newvideo