Cluster Analysis and Unsupervised Machine Learning in Python
What you will learn

Understand the regular KMeans algorithm

Understand and enumerate the disadvantages of KMeans Clustering

Understand the soft or fuzzy KMeans Clustering algorithm

Implement Soft KMeans Clustering in Code

Understand Hierarchical Clustering

Explain algorithmically how Hierarchical Agglomerative Clustering works

Apply Scipy's Hierarchical Clustering library to data

Understand how to read a dendrogram

Understand the different distance metrics used in clustering

Understand the difference between single linkage, complete linkage, Ward linkage, and UPGMA

Understand the Gaussian mixture model and how to use it for density estimation

Write a GMM in Python code

Explain when GMM is equivalent to KMeans Clustering

Explain the expectationmaximization algorithm

Understand how GMM overcomes some disadvantages of KMeans

Understand the Singular Covariance problem and how to fix it
Curriculum
Section 1: Introduction to Unsupervised Learning
Section 2: KMeans Clustering
Section 3: Hierarchical Clustering
Section 4: Gaussian Mixture Models (GMMs)
Section 5: Appendix
Course Description
Data science techniques for pattern recognition, data mining, kmeans clustering, and hierarchical clustering, and KDE.
Requirements
 Know how to code in Python and Numpy
 Install Numpy and Scipy
 Matrix arithmetic, probability
Description
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 realworld environment, you can imagine that a robot or 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 analyticsautomating 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: kmeans 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 kmeans clustering are exactly the same! 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.
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.
HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

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.
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

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)
Who this course is for:
 Students and professionals interested in machine learning and data science
 People who want an introduction to unsupervised machine learning and cluster analysis
 People who want to know how to write their own clustering code
 Professionals interested in data mining big data sets to look for patterns automatically