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! 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.
NOTES:
All the code for this course can be downloaded from my github: https://github.com/lazyprogrammer/machine_learning_examples
In the directory: unsupervised_class
Make sure you always "git pull" so you have the latest version!
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.
USEFUL COURSE ORDERING:
- Linear Regression in Python
- Logistic Regression in Python
- (Supervised Machine Learning in Python)
- Deep Learning in Python
- Practical Deep Learning in Theano and TensorFlow
- Convolutional Neural Networks in Python
- (Easy NLP)
- (Cluster Analysis and Unsupervised Machine Learning)
- Unsupervised Deep Learning
- (Hidden Markov Models)
- Recurrent Neural Networks in Python
- Natural Language Processing with Deep Learning in Python
