Learn Cluster Analysis and Unsupervised Machine Learning in Python Free Video Course

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

Learn Cluster Analysis and Unsupervised Machine Learning in Python Free Video Course

What you will 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

Requirements for this Course:

  • Know how to code in Python and Numpy
  • Install Numpy and Scipy
  • Matrix arithmetic, probability


Group examination is a staple of unaided AI and information science.

It is exceptionally helpful for information mining and large information since it consequently discovers designs in the information, without the requirement for names, not at all like administered AI.

In a genuine climate, you can envision that a robot or a man-made consciousness will not generally approach the ideal answer, or possibly there is definitely not an ideal right answer. You’d need that robot to have the option to investigate the world all alone, and learn things just by searching for designs.

Do you at any point can’t help thinking about how we get the information that we use in our directed AI calculations?

We generally appear to have a pleasant CSV or a table, total with Xs and comparing Ys.

In the event that you haven’t been engaged with gaining information yourself, you probably won’t have contemplated this, however somebody needs to make this information!

Those “Y”s need to come from some place, and a great deal of the time that includes physical work.

In some cases, you don’t approach this sort of data or it is infeasible or expensive to get.

Be that as it may, you actually need to have some thought of the construction of the information. In case you’re doing information investigation mechanizing design acknowledgment in your information would be important.

This is the place where unaided AI becomes an integral factor.

In this course we are first going to discuss bunching. This is the place where as opposed to preparing on marks, we attempt to make our own names! We’ll do this by gathering information that appears to be indistinguishable.

There are 2 strategies for grouping we’ll discuss: k-implies bunching and various leveled grouping.

Then, on the grounds that in AI we like to discuss likelihood conveyances, we’ll go into Gaussian blend models and portion thickness assessment, where we talk about how to “learn” the likelihood appropriation of a bunch of information.

One intriguing actuality is that under specific conditions, Gaussian blend models and k-implies bunching are by and large something similar! We’ll demonstrate how this is the situation.

Every one of the calculations we’ll discuss in this course are staples in AI and information science, so in the event that you need to realize how to consequently discover designs in your information with information mining and example extraction, without requiring somebody to place in manual work to mark that information, then, at that point this course is for you.

Every one of the materials for this course are FREE. You can download and introduce Python, Numpy, and Scipy with basic orders on Windows, Linux, or Mac.

This course centers around “how to construct and comprehend”, not only “how to utilize”. Anybody can figure out how to utilize an API shortly subsequent to perusing some documentation. It’s not tied in with “recollecting realities”, it’s tied in with “seeing with your own eyes” through experimentation. It will show you how to imagine what’s going on in the model inside. In the event that you need something other than a shallow glance at AI models, this course is for you.

“In the event that you can’t carry out it, you don’t get it”

Or on the other hand as the incredible physicist Richard Feynman said: “What I can’t make, I don’t comprehend”.

My courses are the ONLY courses where you will figure out how to carry out AI calculations without any preparation

Different courses will show you how to connect your information into a library, yet do you truly require assist with 3 lines of code?

In the wake of doing likewise with 10 datasets, you understand you didn’t learn 10 things. You learned 1 thing, and just rehashed similar 3 lines of code multiple times…

Recommended Prerequisites:

grid expansion, duplication


Python coding: if/else, circles, records, dicts, sets

Numpy coding: grid and vector tasks, stacking a CSV document


Look at the talk “AI and AI Prerequisite Roadmap” (accessible in the FAQ 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

Course Content:

Introduction to Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Gaussian Mixture Models (GMMs)
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding For Begineers (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix/FAQ Finale

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