K-Means Clustering Algorithm | Elbow Method | Choosing Optimal K

K-Means from Scratch - “K-Means Implementation from Scratch” : https://youtu.be/1m-alNQeEKs ML Models from Scratch Playlist : https://www.youtube.com/playlist?list=PLz6pthWWCdfR5mFMpF39dBuUueQFLZ910 Learn about the Elbow Method in K-Means Clustering algorithm, a crucial technique in unsupervised machine learning. In this video, we'll dive into the concept of the Elbow Method, its importance in determining the optimal number of clusters in K-Means Clustering, and how it can be applied to real-world problems. Whether you're a data scientist, machine learning engineer, or simply interested in data analysis, this video is perfect for you. So, let's get started and explore the Elbow Method in K-Means Clustering! 🔑 What you'll learn: • What is K-Means Clustering? • Understanding the Elbow Method and its significance. • Step-by-step implementation with a hands-on example. • Tips for improving clustering performance. 🎯 Who is this for? Whether you're a beginner in data science or looking to refine your machine learning techniques, this video provides clear explanations and practical demonstrations. Resources: 🔗 Code : https://github.com/mohangollapalli/ml_models/tree/4533730bde7f01ed19d110be5181d347e0c9786b/KMeans #datasciencetutorial #machinelearningtutorial #datascience #mlclustering #machinelearningprojects #datascienceprojects #kmeans #kmeansclustering #elbowmethod #unsupervisedlearning #kmeansalgorithm #SimplifiedAICourse Time breaks: 00:00 Intro. & K-Means algorithm Steps 01:14 Assignment Step 02:13 Step 5, Convergence 03:18 Reading data, extracting numeric features 04:50 Data Normalization 06:29 Creating K-Means model. 07:24 WCSS 10:24 WCSS for different K values. 11:03 Selecting K using elbow curve 15:40 Drawbacks of Inertia. Make sure to subscribe and hit the notification bell to get the latest on deep learning, machine learning, and Data Science tutorials! 💻 @SimplifiedAICourse