Cluster Analysis and Unsupervised Machine Learning in Python

Course details

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 wont always have access to the optimal answer, or maybe there isnt an optimal correct answer. Youd 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 havent been involved in acquiring data yourself, you might not have thought about this, but someonehas to make this data!

Those Ys have to come from somewhere, and a lot of the time that involves manual labor.

Sometimes, you dont 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 dataanalytics automatingpattern 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! Well do this by grouping together data that looks alike.

There are 2 methods of clustering well talk about: k-means clustering and hierarchical clustering.

Next, because in machine learning we like to talk about probability distributions, well 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! Well prove how this is the case.

All the algorithms well 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 workto label that data, then this course is for you.

All the materials for this course are FREE. You can download and install Python, Numpy, andScipy 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 wantmorethan just a superficial look at machine learning models, this course is for you.

NOTES:

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: unsupervised_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES /KNOWLEDGEYOUARE ASSUMEDTOHAVE:

• calculus
• linear algebra
• probability
• Python coding: if/else, loops, lists, dicts, sets
• Numpy coding: matrix and vector operations, loading a CSV file

TIPS (for getting through the course):

• Watch it at 2x.
• Take handwritten notes. This will drastically increase your ability to retain the information.
• Write down the equations. If you don't, I guarantee it will just look like gibberish.
• Ask lots of questions on the discussion board. The more the better!
• Realize that most exercises will take you days or weeks to complete.
• Write code yourself, don't just sit there and look at my code.

WHATORDERSHOULDITAKEYOURCOURSESIN?:

• Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)