April 20, 2016

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**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** won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’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 haven’t been involved in acquiring data yourself, you might not have thought about this, but someone has to make this data!

Those “Y”s have to come from somewhere, and a lot of the time that involves manual labor.

Sometimes, you don’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! We’ll do this by grouping together data that looks alike.

There are 2 methods of clustering we’ll talk about: **k-means clustering** and **hierarchical clustering**.

Next, because in machine learning we like to talk about probability distributions, we’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! You can think of GMMs as a “souped up” version of k-means. We’ll prove how this is the case.

All the algorithms we’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**.

50% OFF COUPON: https://www.udemy.com/cluster-analysis-unsupervised-machine-learning-python/?couponCode=EARLYBIRD

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