Mean Shift Intro - Practical Machine Learning Tutorial with Python p.39

Channel:
United States sentdex
Subscribers:
1,410,000
Published on ● Video Link: https://www.youtube.com/watch?v=3ERPpzrDkVg


Category:
Tutorial
Duration: 11:42
58,959 views
796

Mean Shift is very similar to the K-Means algorithm, except for one very important factor: you do not need to specify the number of groups prior to training. The Mean Shift algorithm finds clusters on its own. For this reason, it is even more of an "unsupervised" machine learning algorithm than K-Means.

The way Mean Shift works is to go through each featureset (a datapoint on a graph), and proceed to do a hill climb operation. Hill Climbing is just as it sounds: The idea is to continually increase, or go up, until you cannot anymore. We don't have for sure just one local maximal value. We might have only one, or we might have ten. Our "hill" in this case will be the number of featuresets/datapoints within a given radius. The radius is also called a bandwidth, and the entire window is your Kernel. The more data within the window, the better. Once we can no longer take another step without decreasing the number of featuresets/datapoints within the radius, we take the mean of all data in that region and we have located a cluster center. We do this starting from each data point. Many data points will lead to the same cluster center, which should be expected, but it is also possible that other data points will take you to a completely separate cluster center.

https://pythonprogramming.net
https://twitter.com/sentdex
https://www.facebook.com/pythonprogramming.net/
https://plus.google.com/+sentdex



Other Videos By sentdex


2016-07-21Running our Network - Deep Learning with Neural Networks and TensorFlow
2016-07-20Neural Network Model - Deep Learning with Neural Networks and TensorFlow
2016-07-19MSRC Blue CCW 1.3
2016-07-19TensorFlow Basics - Deep Learning with Neural Networks p. 2
2016-07-18Installing TensorFlow (OPTIONAL) - Deep Learning with Neural Networks and TensorFlow p2.1
2016-07-18Deep Learning with Neural Networks and TensorFlow Introduction
2016-07-12Honda S2000 MSR Cresson CCW trackday 7-10-2016
2016-07-07Mean Shift Dynamic Bandwidth - Practical Machine Learning Tutorial with Python p.42
2016-07-05Mean Shift from Scratch - Practical Machine Learning Tutorial with Python p.41
2016-07-01Mean Shift with Titanic Dataset - Practical Machine Learning Tutorial with Python p.40
2016-06-29Mean Shift Intro - Practical Machine Learning Tutorial with Python p.39
2016-06-22K Means from Scratch - Practical Machine Learning Tutorial with Python p.38
2016-06-20Custom K Means - Practical Machine Learning Tutorial with Python p.37
2016-06-11K Means with Titanic Dataset - Practical Machine Learning Tutorial with Python p.36
2016-06-10Handling Non-Numeric Data - Practical Machine Learning Tutorial with Python p.35
2016-06-07Clustering Introduction - Practical Machine Learning Tutorial with Python p.34
2016-06-02SVM Parameters - Practical Machine Learning Tutorial with Python p.33
2016-06-01Soft Margin SVM and Kernels with CVXOPT - Practical Machine Learning Tutorial with Python p.32
2016-05-31Soft Margin SVM - Practical Machine Learning Tutorial with Python p.31
2016-05-30Why Kernels - Practical Machine Learning Tutorial with Python p.30
2016-05-29Kernels Introduction - Practical Machine Learning Tutorial with Python p.29


Tags:
mean shift
python
clustering
flat clustering
hierarchical clustering
unsupervised machine learning
machine learning
tutorial
artificial intelligence
scikit-learn
sklearn
tensorflow
theano