Cluster analysis is a staple of unsupervised machine learning and data science.
It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning.
In a real-world environment, you can imagine that a robot or an artificial intelligence wonâ€™t always have access to the optimal answer, or maybe there isnâ€™t an optimal correct answer. Youâ€™d want that robot to be able to explore the world on its own, and learn things just by looking for patterns.
Do you ever wonder how we get the data that we use in our supervised machine learning algorithms?
We always seem to have a nice CSV or a table, complete with Xs and corresponding Ys.
If you havenâ€™t been involved in acquiring data yourself, you might not have thought about this, but someone has to make this data!
Those â€śYâ€ťs have to come from somewhere, and a lot of the time that involves manual labor.
Sometimes, you donâ€™t have access to this kind of information or it is infeasible or costly to acquire.
But you still want to have some idea of the structure of the data. If you’re doing data analytics automating pattern recognition in your data would be invaluable.
This is where unsupervised machine learning comes into play.
In this course we are first going to talk about clustering. This is where instead of training on labels, we try to create our own labels! Weâ€™ll do this by grouping together data that looks alike.
There are 2 methods of clustering weâ€™ll talk about: k-means clustering and hierarchical clustering.
Next, because in machine learning we like to talk about probability distributions, weâ€™ll go into Gaussian mixture models and kernel density estimation, where we talk about how to “learn” the probability distribution of a set of data.
One interesting fact is that under certain conditions, Gaussian mixture models and k-means clustering are exactly the same! Weâ€™ll prove how this is the case.
All the algorithms weâ€™ll talk about in this course are staples in machine learning and data science, so if you want to know how to automatically find patterns in your data with data mining and pattern extraction, without needing someone to put in manual work to label that data, then this course is for you.
All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.
This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
“If you can’t implement it, you don’t understand it”
Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…
Suggested Prerequisites:
matrix addition, multiplication
probability
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)
This lecture describes what unsupervised machine learning (not just clustering) is used for in general.
There are 2 major categories:
1) density estimation
If we can figure out the probability distribution of the data, not only is this a model of the data, but we can then sample from the distribution to generate new data.
For example, we can train a model to read lots of Shakespeare and then generate writing in the style of Shakespeare.
2) latent variables
This allows us to find the underlying cause of the data we've observed by reducing it to a small set of factors.
For example, if we measure the heights of all the people in our class and plot them on a histogram, we may notice 2 "bumps".
These "bumps" correspond to male heights and female heights.
Thus, being male or female is the hidden cause of higher / lower height values.
Clustering does exactly this - it tells us how the data can be split up into distinct groups / segments / categories.
Unsupervised machine learning can also be used for:
dimensionality reduction - modern datasets can have millions of features, but many of them may be correlated
visualization - you can't see a million-dimensional dataset, but if you reduce the dimensionality to 2, then it can be visualized
Learn about the different possible distance metrics that can be used for both k-means and agglomerative clustering, and what constitutes a valid distance metric. Learn about the different linkage methods for hierarchical clustering, like single linkage, complete linkage, UPGMA, and Ward linkage.