Cluster Analysis and Unsupervised Machine Learning in Python

Cluster Analysis and Unsupervised Machine Learning in Python, available at $24.99, has an average rating of 4.7, with 60 lectures, based on 5045 reviews, and has 28706 subscribers.

You will learn about 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 This course is ideal for individuals who are Students and professionals interested in machine learning and data science or People who want an introduction to unsupervised machine learning and cluster analysis or People who want to know how to write their own clustering code or Professionals interested in data mining big data sets to look for patterns automatically It is particularly useful for Students and professionals interested in machine learning and data science or People who want an introduction to unsupervised machine learning and cluster analysis or People who want to know how to write their own clustering code or Professionals interested in data mining big data sets to look for patterns automatically.

Enroll now: Cluster Analysis and Unsupervised Machine Learning in Python

Summary

Title: Cluster Analysis and Unsupervised Machine Learning in Python

Price: $24.99

Average Rating: 4.7

Number of Lectures: 60

Number of Published Lectures: 57

Number of Curriculum Items: 60

Number of Published Curriculum Objects: 57

Original Price: $24.99

Quality Status: approved

Status: Live

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

Who Should Attend

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

Target Audiences

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

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)

Course Curriculum

Chapter 1: Introduction to Unsupervised Learning

Lecture 1: Introduction

Lecture 2: Course Outline

Lecture 3: What is unsupervised learning used for?

Lecture 4: Why Use Clustering?

Lecture 5: Where to get the code

Lecture 6: How to Succeed in this Course

Chapter 2: K-Means Clustering

Lecture 1: An Easy Introduction to K-Means Clustering

Lecture 2: Hard K-Means: Exercise Prompt 1

Lecture 3: Hard K-Means: Exercise 1 Solution

Lecture 4: Hard K-Means: Exercise Prompt 2

Lecture 5: Hard K-Means: Exercise 2 Solution

Lecture 6: Hard K-Means: Exercise Prompt 3

Lecture 7: Hard K-Means: Exercise 3 Solution

Lecture 8: Hard K-Means Objective: Theory

Lecture 9: Hard K-Means Objective: Code

Lecture 10: Soft K-Means

Lecture 11: The Soft K-Means Objective Function

Lecture 12: Soft K-Means in Python Code

Lecture 13: How to Pace Yourself

Lecture 14: Visualizing Each Step of K-Means

Lecture 15: Examples of where K-Means can fail

Lecture 16: Disadvantages of K-Means Clustering

Lecture 17: How to Evaluate a Clustering (Purity, Davies-Bouldin Index)

Lecture 18: Using K-Means on Real Data: MNIST

Lecture 19: One Way to Choose K

Lecture 20: K-Means Application: Finding Clusters of Related Words

Lecture 21: Clustering for NLP and Computer Vision: Real-World Applications

Lecture 22: Suggestion Box

Chapter 3: Hierarchical Clustering

Lecture 1: Visual Walkthrough of Agglomerative Hierarchical Clustering

Lecture 2: Agglomerative Clustering Options

Lecture 3: Using Hierarchical Clustering in Python and Interpreting the Dendrogram

Lecture 4: Application: Evolution

Lecture 5: Application: Donald Trump vs. Hillary Clinton Tweets

Chapter 4: Gaussian Mixture Models (GMMs)

Lecture 1: Gaussian Mixture Model (GMM) Algorithm

Lecture 2: Write a Gaussian Mixture Model in Python Code

Lecture 3: Practical Issues with GMM / Singular Covariance

Lecture 4: Comparison between GMM and K-Means

Lecture 5: Kernel Density Estimation

Lecture 6: GMM vs Bayes Classifier (pt 1)

Lecture 7: GMM vs Bayes Classifier (pt 2)

Lecture 8: Expectation-Maximization (pt 1)

Lecture 9: Expectation-Maximization (pt 2)

Lecture 10: Expectation-Maximization (pt 3)

Lecture 11: Future Unsupervised Learning Algorithms You Will Learn

Chapter 5: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Chapter 6: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Lecture 3: Proof that using Jupyter Notebook is the same as not using it

Lecture 4: Python 2 vs Python 3

Chapter 7: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)

Chapter 8: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 52 votes
2 stars: 61 votes
3 stars: 305 votes
4 stars: 1739 votes
5 stars: 2888 votes

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