Cluster Analysis and Unsupervised Machine Learning in Python

Cluster Analysis and Unsupervised Machine Learning in Python

Bestselling
Bestselling
Created by Lazy Programmer Inc.
Last updated 6/2017
English
Created by Lazy Programmer Inc.
Created by Lazy Programmer Inc.
Last updated 6/2017
Last updated 6/2017
Last updated 6/2017
English
What Will I 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
What Will I Learn?
Requirements
  • Know how to code in Python and Numpy
  • Install Numpy and Scipy
Requirements
  • Know how to code in Python and Numpy
  • Install Numpy and Scipy
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.

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 / 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.

USEFUL COURSE ORDERING:

  • (The Numpy Stack in Python)
  • Linear Regression in Python
  • Logistic Regression in Python
  • (Supervised Machine Learning in Python)
  • (Bayesian Machine Learning in Python: A/B Testing)
  • Deep Learning in Python
  • Practical Deep Learning in Theano and TensorFlow
  • (Supervised Machine Learning in Python 2: Ensemble Methods)
  • Convolutional Neural Networks in Python
  • (Easy NLP)
  • (Cluster Analysis and Unsupervised Machine Learning)
  • Unsupervised Deep Learning
  • (Hidden Markov Models)
  • Recurrent Neural Networks in Python
  • Artificial Intelligence: Reinforcement Learning in Python
  • Natural Language Processing with Deep Learning in Python
Who is the target audience?
  • 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

Size: 310.81M

Description
Who is the target audience?
  • 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

Size: 310.81M

Who is the target audience?

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