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Cluster Analysis and Unsupervised Machine Learning in Python

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.
Instructor:
Lazy Programmer Inc.
13,279 students enrolled
English [Auto-generated] More
Understand the regular K-Means algorithm
Understand and enumerate the disadvantages of K-Means Clustering
Understand the soft or fuzzy K-Means Clustering algorithm
Implement Soft K-Means 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 K-Means Clustering
Explain the expectation-maximization algorithm
Understand how GMM overcomes some disadvantages of K-Means
Understand the Singular Covariance problem and how to fix it

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.

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)

Introduction to Unsupervised Learning

1
Introduction and Outline
2
What is unsupervised learning used for?
3
Why Use Clustering?
4
How to Succeed in this Course

K-Means Clustering

1
An Easy Introduction to K-Means Clustering
2
Visual Walkthrough of the K-Means Clustering Algorithm
3
Soft K-Means
4
The K-Means Objective Function
5
Soft K-Means in Python Code
6
Visualizing Each Step of K-Means
7
Examples of where K-Means can fail
8
Disadvantages of K-Means Clustering
9
How to Evaluate a Clustering (Purity, Davies-Bouldin Index)
10
Using K-Means on Real Data: MNIST
11
One Way to Choose K
12
K-Means Application: Finding Clusters of Related Words

Hierarchical Clustering

1
Visual Walkthrough of Agglomerative Hierarchical Clustering
2
Agglomerative Clustering Options

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.

3
Using Hierarchical Clustering in Python and Interpreting the Dendrogram
4
Application: Evolution
5
Application: Donald Trump vs. Hillary Clinton Tweets

Gaussian Mixture Models (GMMs)

1
Description of the Gaussian Mixture Model and How to Train a GMM
2
Comparison between GMM and K-Means
3
Write a Gaussian Mixture Model in Python Code
4
Practical Issues with GMM / Singular Covariance
5
Kernel Density Estimation
6
Expectation-Maximization
7
Future Unsupervised Learning Algorithms You Will Learn

Appendix

1
What is the Appendix?
2
Windows-Focused Environment Setup 2018
3
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
4
How to Code by Yourself (part 1)
5
How to Code by Yourself (part 2)
6
How to Succeed in this Course (Long Version)
7
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
8
Proof that using Jupyter Notebook is the same as not using it
9
Python 2 vs Python 3
10
What order should I take your courses in? (part 1)
11
What order should I take your courses in? (part 2)
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