Cluster Analysis and Unsupervised Machine Learning in Python (

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

Created by: Lazy Programmer Inc.

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

Quality Score

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Overall Score : 82 / 100

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Instructor Details

Lazy Programmer Inc.

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.
I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.
Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.
I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.
My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.
Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL



50 total reviews

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I found the class to be great! However, I did struggle because I figured I didn't really need to prereqs! Well you really do, I will brush up on my Calculus and retake this class!

No, it was not a good match. Concepts are not well explained.

Provided a great intuition for cluster analysis. It explained all the different methods and the advantages and disadvantages of each. It was applied to a lot of situations from biology to NLP. Thanks for the course

I'm really enjoying this lecture series. Some of it is a bit more advanced than I'm ready for, but I'm doing my research to make sure I understand everything as well as I can.

I take these courses since they focus on the math behind these algos - without having to use them blindly. Understanding the math is also crucial to tuning these algos to perform optimally on your dataset.

I often find myself skipping videos because most teachers assume the student doesn't have the knowledge. This one gave me enough depth to carry out my own clustering experiments without wasting time on introductory videos.

Great stuff. Instructor speaks Mathematics like it's his native language. I wish there was more explanation and examples (at a little slower pace) to help me grasp the materials better. I recommend the course for the intermediate/advanced person who wishes to learn unsupervised clustering.

The course covered everything I was expecting, however it would be great to have more real examples in which the clustering techniques can be applied.

Great course. Good emphasis on practicals. Make sure you read and complete the prequiaites and index to get started. Love it.

Most of slides are too much textual and sometimes the instructor seems to be reading them.

Good explanation of Soft K-Means Clustering and Hierarchical Clustering with clearly explained code examples.Gaussian Mixture Models could have used more explanations.

Thanks for helping me to understand clustering with Python. Before this class I only had some rough ideas but after this course I feel like an expert.