K-Means - Explained
K-Means Clustering is one of the most important unsupervised learning algorithms in machine learning and data science. This video explains how k-means works step by step, including centroid initialization, the assignment step, the update step, convergence, objective function minimization, and sensitivity to initialization. Perfect for beginners learning clustering, machine learning algorithms, data analysis, and pattern recognition.
*Related Videos*
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The Hessian Matrix: https://youtu.be/9tp1kULwU2w
The Jacobian Matrix: https://youtu.be/6FesMicc844
Bayesian Optimization: https://youtu.be/Kq6_kzlwSUQ
Hyperparameters Tuning: Grid Search vs Random Search: https://youtu.be/G-fXV-o9QV8
The Kernel Trick: https://youtu.be/N_RQj4OL1mg
Cross-Entropy - Explained: https://youtu.be/Fv98vtitmiA
Dropout - Explained: https://youtu.be/FDF_Q3_98GQ
Overfitting vs Underfitting: https://youtu.be/B9rhzg6_LLw
Why Models Overfit and Underfit - The Bias Variance Trade-off: https://youtu.be/5mbX6ITznHk
Least Squares vs Maximum Likelihood: https://youtu.be/WCP98USBZ0w
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