Cluster Analysis and Unsupervised Machine Learning in Python
4.5 (3,518 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
18,936 students enrolled

Cluster Analysis and Unsupervised Machine Learning in Python

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.
Highest Rated
4.5 (3,518 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
18,936 students enrolled
Last updated 5/2020
English
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Current price: $83.99 Original price: $119.99 Discount: 30% off
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This course includes
  • 6.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • 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
Course content
Expand all 46 lectures 06:18:19
+ Introduction to Unsupervised Learning
6 lectures 32:17
Course Outline
04:34

This lecture describes what unsupervised machine learning (not just clustering) is used for in general.

There are 2 major categories:


1) density estimation

If we can figure out the probability distribution of the data, not only is this a model of the data, but we can then sample from the distribution to generate new data.

For example, we can train a model to read lots of Shakespeare and then generate writing in the style of Shakespeare.


2) latent variables

This allows us to find the underlying cause of the data we've observed by reducing it to a small set of factors.

For example, if we measure the heights of all the people in our class and plot them on a histogram, we may notice 2 "bumps".

These "bumps" correspond to male heights and female heights.

Thus, being male or female is the hidden cause of higher / lower height values.

Clustering does exactly this - it tells us how the data can be split up into distinct groups / segments / categories.


Unsupervised machine learning can also be used for:

  • dimensionality reduction - modern datasets can have millions of features, but many of them may be correlated

  • visualization - you can't see a million-dimensional dataset, but if you reduce the dimensionality to 2, then it can be visualized

Preview 05:31
Where to get the code
04:36
How to Succeed in this Course
03:13
+ K-Means Clustering
12 lectures 01:04:56
Visual Walkthrough of the K-Means Clustering Algorithm
02:58
Soft K-Means
05:41
The K-Means Objective Function
01:39
Soft K-Means in Python Code
10:03
Visualizing Each Step of K-Means
02:18
Examples of where K-Means can fail
07:32
Disadvantages of K-Means Clustering
02:13
How to Evaluate a Clustering (Purity, Davies-Bouldin Index)
06:33
Using K-Means on Real Data: MNIST
05:00
One Way to Choose K
05:15
K-Means Application: Finding Clusters of Related Words
08:38
+ Hierarchical Clustering
5 lectures 43:25
Visual Walkthrough of Agglomerative Hierarchical Clustering
02:35

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.

Agglomerative Clustering Options
03:38
Using Hierarchical Clustering in Python and Interpreting the Dendrogram
04:38
Application: Evolution
14:00
Application: Donald Trump vs. Hillary Clinton Tweets
18:34
+ Gaussian Mixture Models (GMMs)
11 lectures 01:29:13
Gaussian Mixture Model (GMM) Algorithm
15:31
Write a Gaussian Mixture Model in Python Code
09:59
Practical Issues with GMM / Singular Covariance
09:07
Comparison between GMM and K-Means
03:55
Kernel Density Estimation
06:24
GMM vs Bayes Classifier (pt 1)
09:28
GMM vs Bayes Classifier (pt 2)
11:30
Expectation-Maximization (pt 1)
11:45
Expectation-Maximization (pt 2)
02:24
Expectation-Maximization (pt 3)
08:09
Future Unsupervised Learning Algorithms You Will Learn
01:01
+ Appendix / FAQ
12 lectures 02:28:28
What is the Appendix?
02:48
Windows-Focused Environment Setup 2018
20:20
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:32
How to Code by Yourself (part 1)
15:54
How to Code by Yourself (part 2)
09:23
How to Succeed in this Course (Long Version)
10:24
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
What order should I take your courses in? (part 1)
11:18
What order should I take your courses in? (part 2)
16:07
BONUS: Where to get discount coupons and FREE deep learning material
05:31
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 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.



Suggested Prerequisites:

  • matrix addition, multiplication

  • 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