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: 64
Length: 8h 22m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

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".

Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...


I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...


By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.


Introduction to Unsupervised Learning

6 Lectures · 32min
  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 (03:04)

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 (03:10)

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)

3 Lectures · 42min
  1. Pre-Installation Check (04:13)
  2. Anaconda Environment Setup (20:21)
  3. 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 (05:49)
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