Cluster Analysis and Unsupervised Machine Learning in Python

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.

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Course Data

Lectures: 63
Length: 8h 20m
Skill Level: All Levels
Languages: English
Includes: Lifetime access

Course 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:

  • calculus
  • linear algebra
  • probability
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations, loading a CSV file

Tips for success:

  • Use the video speed changer! Personally, I like to watch 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!
  • Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
  • Write code yourself, this is an applied course! Don't be a "couch potato".


Introduction to Unsupervised Learning

6 Lectures · 34min
  1. Introduction (05:03) (FREE preview available)
  2. Course Outline (04:34)
  3. What is unsupervised learning used for? (05:31)
  4. Why Use Clustering? (09:20)
  5. Where to get the code (04:36)
  6. How to Succeed in this Course (05:52)

K-Means Clustering

23 Lectures · 02hr 32min
  1. An Easy Introduction to K-Means Clustering (07:07)
  2. Hard K-Means: Exercise Prompt 1 (09:13)
  3. Hard K-Means: Exercise 1 Solution (11:09)
  4. Hard K-Means: Exercise Prompt 2 (05:04)
  5. Hard K-Means: Exercise 2 Solution (07:08)
  6. Hard K-Means: Exercise Prompt 3 (06:55)
  7. Hard K-Means: Exercise 3 Solution (16:22)
  8. Hard K-Means Objective: Theory (13:01)
  9. Hard K-Means Objective: Code (05:13)
  10. Visual Walkthrough of the K-Means Clustering Algorithm (Legacy) (02:59)
  11. Soft K-Means (05:42)
  12. The K-Means Objective Function (01:40)
  13. Soft K-Means in Python Code (10:04)
  14. How to Pace Yourself (03:19)
  15. Visualizing Each Step of K-Means (02:19)
  16. Examples of where K-Means can fail (07:33)
  17. Disadvantages of K-Means Clustering (02:14)
  18. How to Evaluate a Clustering (Purity, Davies-Bouldin Index) (06:34)
  19. Using K-Means on Real Data: MNIST (05:01)
  20. One Way to Choose K (05:16)
  21. K-Means Application: Finding Clusters of Related Words (08:39)
  22. Clustering for NLP and Computer Vision: Real-World Applications (06:58)
  23. Suggestion Box (03:03)

Hierarchical Clustering

5 Lectures · 43min
  1. Visual Walkthrough of Agglomerative Hierarchical Clustering (02:36)
  2. Agglomerative Clustering Options (03:39)
  3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram (04:39)
  4. Application: Evolution (14:01)
  5. Application: Donald Trump vs. Hillary Clinton Tweets (18:35)

Gaussian Mixture Models (GMMs)

10 Lectures · 01hr 37min
  1. Gaussian Mixture Model (GMM) Algorithm (15:31)
  2. Write a Gaussian Mixture Model in Python Code (18:54)
  3. Practical Issues with GMM / Singular Covariance (09:07)
  4. Comparison between GMM and K-Means (03:55)
  5. Kernel Density Estimation (06:24)
  6. GMM vs Bayes Classifier (pt 1) (09:28)
  7. GMM vs Bayes Classifier (pt 2) (11:30)
  8. Expectation-Maximization (pt 1) (11:45)
  9. Expectation-Maximization (pt 2) (02:24)
  10. Expectation-Maximization (pt 3) (08:09)

Gaussian Mixture Models (GMMs) (Legacy)

7 Lectures · 23min
  1. Description of the Gaussian Mixture Model and How to Train a GMM (03:05)
  2. Comparison between GMM and K-Means (01:45)
  3. Write a Gaussian Mixture Model in Python Code (10:00)
  4. Practical Issues with GMM / Singular Covariance (02:56)
  5. Kernel Density Estimation (02:11)
  6. Expectation-Maximization (02:02)
  7. Future Unsupervised Learning Algorithms You Will Learn (01:02)

Setting Up Your Environment (Appendix/FAQ by Student Request)

2 Lectures · 37min
  1. Windows-Focused Environment Setup 2018 (20:21)
  2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

4 Lectures · 42min
  1. How to Code Yourself (part 1) (15:55)
  2. How to Code Yourself (part 2) (09:24)
  3. Proof that using Jupyter Notebook is the same as not using it (12:29)
  4. Python 2 vs Python 3 (04:38)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:31)
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