Udemy - Cluster Analysis and Unsupervised Machine Learning in Python

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.9 GB
  • Uploaded Bytutsnode
  • Downloads125
  • Last checkedDec. 15th '20
  • Date uploadedDec. 12th '20
  • Seeders 16
  • Leechers7
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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.

“If you can’t implement it, you don’t understand it”

Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…

Suggested Prerequisites:

matrix addition, multiplication
probability
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available 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

Requirements

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

Last Updated 11/2020

Files:

Cluster Analysis and Unsupervised Machine Learning in Python [TutsNode.com] - Cluster Analysis and Unsupervised Machine Learning in Python 5. Setting Up Your Environment (FAQ by Student Request)
  • 1. Windows-Focused Environment Setup 2018.mp4 (186.3 MB)
  • 1. Windows-Focused Environment Setup 2018.srt (20.1 KB)
  • 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (43.9 MB)
  • 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt (14.5 KB)
1. Introduction to Unsupervised Learning
  • 1. Introduction.mp4 (45.6 MB)
  • 1. Introduction.srt (6.9 KB)
  • 2. Course Outline.mp4 (20.3 MB)
  • 2. Course Outline.srt (6.0 KB)
  • 3. What is unsupervised learning used for.mp4 (29.1 MB)
  • 3. What is unsupervised learning used for.srt (7.2 KB)
  • 4. Why Use Clustering.mp4 (54.9 MB)
  • 4. Why Use Clustering.srt (12.1 KB)
  • 5. Where to get the code.mp4 (23.1 MB)
  • 5. Where to get the code.srt (6.3 KB)
  • 5.1 Github Link.html (0.1 KB)
  • 6. Anyone Can Succeed in this Course.mp4 (78.1 MB)
  • 6. Anyone Can Succeed in this Course.srt (17.1 KB)
2. K-Means Clustering
  • 1. An Easy Introduction to K-Means Clustering.mp4 (12.5 MB)
  • 1. An Easy Introduction to K-Means Clustering.srt (9.4 KB)
  • 2. Hard K-Means Exercise Prompt 1.mp4 (50.0 MB)
  • 2. Hard K-Means Exercise Prompt 1.srt (11.5 KB)
  • 3. Hard K-Means Exercise 1 Solution.mp4 (55.4 MB)
  • 3. Hard K-Means Exercise 1 Solution.srt (13.8 KB)
  • 4. Hard K-Means Exercise Prompt 2.mp4 (23.0 MB)
  • 4. Hard K-Means Exercise Prompt 2.srt (6.1 KB)
  • 5. Hard K-Means Exercise 2 Solution.mp4 (33.3 MB)
  • 5. Hard K-Means Exercise 2 Solution.srt (8.4 KB)
  • 6. Hard K-Means Exercise Prompt 3.mp4 (41.8 MB)
  • 6. Hard K-Means Exercise Prompt 3.srt (8.7 KB)
  • 7. Hard K-Means Exercise 3 Solution.mp4 (91.3 MB)
  • 7. Hard K-Means Exercise 3 Solution.srt (20.5 KB)
  • 8. Hard K-Means Objective Theory.mp4 (51.9 MB)
  • 8. Hard K-Means Objective Theory.srt (16.9 KB)
  • 9. Hard K-Means Objective Code.mp4 (27.7 MB)
  • 9. Hard K-Means Objective Code.srt (6.0 KB)
  • 10. Soft K-Means.mp4 (25.3 MB)
  • 10. Soft K-Means.srt (7.0 KB)
  • 11. The Soft K-Means Objective Function.mp4 (3.0 MB)
  • 11. The Soft K-Means Objective Function.srt (2.1 KB)
  • 12. Soft K-Means in Python Code.mp4 (30.2 MB)
  • 12. Soft K-Means in Python Code.srt (7.8 KB)
  • 13. How to Pace Yourself.mp4 (22.4 MB)
  • 13. How to Pace Yourself.srt (4.7 KB)
  • 14. Visualizing Each Step of K-Means.mp4 (5.2 MB)
  • 14. Visualizing Each Step of K-Means.srt (2.7 KB)
  • 15. Examples of where K-Means can fail.mp4 (17.0 MB)
  • 15. Examples of where K-Means can fail.srt (5.2 KB)
  • 16. Disadvantages of K-Means Clustering.mp4 (3.9 MB)
  • 16. Disadvantages of K-Means Clustering.srt (3.3 KB)
  • 17. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).mp4 (11.4 MB)
  • 17. How to Evaluate a Clustering (Purity, Davies-Bouldin Index).srt (9.0 KB)
  • 18. Using K-Means on Real Data MNIST.mp4 (10.7 MB)
  • 18. Using K-Means on Real Data MNIST.srt (7.0 KB)
  • 19. One Way to Choose K.mp4 (9.1 MB)
  • 19. One Way to Choose K.srt (5.1 KB)
  • 20. K-Means Application Finding Clusters of Related Words.mp4 (26.0 MB)
  • 20. K-Means Application Finding Clusters of Related Words.srt (8.4 KB)
  • 21. Clustering for NLP and Computer Vision Real-World Applications.mp4 (42.4 MB)
  • 21. Clustering for NLP and Computer Vision Real-World Applications.srt (9.1 KB)
  • 22. Suggestion Box.mp4 (16.1 MB)
  • 22. Suggestion Box.srt (4.7 KB)
3. Hierarchical Clustering
  • 1. Visual Walkthrough of Agglomerative Hierarchical Clustering.mp4 (4.4 MB)
  • 1. Visual Walkthrough of Agglomerative Hierarchical Clustering.srt (3.5 KB)
  • 2. Agglomerative Clustering Options.mp4 (6.2 MB)
  • 2. Agglomerative Clustering Options.srt (5.4 KB)
  • 3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.mp4 (11.8 MB)
  • 3. Using Hierarchical Clustering in Python and Interpreting the Dendrogram.srt (4.4 KB)
  • 4. Application Evolution.mp4 (26.4 MB)
  • 4. Application Evolution.srt (16.2 KB)
  • 5. Application Donald Trump vs. Hillary Clinton Tweets.mp4 (35.3 MB)
  • 5. Application Donald Trump vs. Hillary Clinton Tweets.srt (19.4 KB)
4. Gaussian Mixture Models (GMMs)
  • 1. Gaussian Mixture Model (GMM) Algorithm.mp4 (65.8 MB)
  • 1. Gaussian Mixture Model (GMM) Algorithm.srt (20.2 KB)
  • 2. Write a Gaussian Mixture Model in Python Code.mp4 (137.5 MB)
  • 2. Write a Gaussian Mixture Model in Python Code.srt (24.9 KB)
  • 3. Practical Issues with GMM Singular Covariance.mp4 (43.3 MB)
  • 3. Practical Issues with GMM Singular Covariance.srt (12.1 KB)
  • 4. Comparison between GMM and K-Means.mp4 (19.2 MB)
  • 4. Comparison between GMM and K-Means.srt (5.0 KB)
  • 5. Kernel Density Estimation.mp4 (29.9 MB)
  • 5. Kernel Density Estimation.srt (8.4 KB)
  • 6. GMM vs Bayes Classifier (pt 1).mp4 (41.3 MB)
  • 6. GMM vs Bayes Classifier (pt 1).srt (12.5 KB)
  • 7. GMM vs Bayes Classifier (pt 2).mp4 (45.2 MB)
  • 7. GMM vs Bayes Classifier (pt 2).srt (14.6 KB)
  • 8. Expectation-Maximization (pt 1).mp4 (49.8 MB)
  • 8. Expectation-Maximization (pt 1).srt (14.9 KB)
  • 9. Expectation-Maximization (pt 2).mp4 (10.9 MB)
  • 9. Expectation-Maximization (pt 2).srt (2.6 KB)
  • 10. Expectation-Maximization (pt 3).mp4 (31.3 MB)
  • 10. Expectation-Maximization (pt 3).srt (10.1 KB)
  • 11. Future Unsupervised Learning Algorithms You Will Learn.mp4 (2.0 MB)
  • 11. Future Unsupervised Learning Algorithms You Will Learn.srt (1.4 KB)
6. Extra Help With Python Coding for Beginners (FAQ by Student Request)
  • 1. How to Code by Yourself (part 1).mp4 (24.5 MB)
  • 1. How to Code by Yourself (part 1).srt (22.8 KB)
  • 2. How to Code by Yourself (part 2).mp4 (14.8 MB)
  • 2. How to Code by Yourself (part 2).srt (13.3 KB)

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