Udemy
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
Development
Web Development Data Science Mobile Development Programming Languages Game Development Database Design & Development Software Testing Software Engineering Development Tools No-Code Development
Business
Entrepreneurship Communications Management Sales Business Strategy Operations Project Management Business Law Business Analytics & Intelligence Human Resources Industry E-Commerce Media Real Estate Other Business
Finance & Accounting
Accounting & Bookkeeping Compliance Cryptocurrency & Blockchain Economics Finance Finance Cert & Exam Prep Financial Modeling & Analysis Investing & Trading Money Management Tools Taxes Other Finance & Accounting
IT & Software
IT Certification Network & Security Hardware Operating Systems Other IT & Software
Office Productivity
Microsoft Apple Google SAP Oracle Other Office Productivity
Personal Development
Personal Transformation Personal Productivity Leadership Career Development Parenting & Relationships Happiness Esoteric Practices Religion & Spirituality Personal Brand Building Creativity Influence Self Esteem & Confidence Stress Management Memory & Study Skills Motivation Other Personal Development
Design
Web Design Graphic Design & Illustration Design Tools User Experience Design Game Design Design Thinking 3D & Animation Fashion Design Architectural Design Interior Design Other Design
Marketing
Digital Marketing Search Engine Optimization Social Media Marketing Branding Marketing Fundamentals Marketing Analytics & Automation Public Relations Advertising Video & Mobile Marketing Content Marketing Growth Hacking Affiliate Marketing Product Marketing Other Marketing
Lifestyle
Arts & Crafts Beauty & Makeup Esoteric Practices Food & Beverage Gaming Home Improvement Pet Care & Training Travel Other Lifestyle
Photography & Video
Digital Photography Photography Portrait Photography Photography Tools Commercial Photography Video Design Other Photography & Video
Health & Fitness
Fitness General Health Sports Nutrition Yoga Mental Health Dieting Self Defense Safety & First Aid Dance Meditation Other Health & Fitness
Music
Instruments Music Production Music Fundamentals Vocal Music Techniques Music Software Other Music
Teaching & Academics
Engineering Humanities Math Science Online Education Social Science Language Teacher Training Test Prep Other Teaching & Academics
AWS Certification Microsoft Certification AWS Certified Solutions Architect - Associate AWS Certified Cloud Practitioner CompTIA A+ Cisco CCNA Amazon AWS CompTIA Security+ AWS Certified Developer - Associate
Graphic Design Photoshop Adobe Illustrator Drawing Digital Painting InDesign Character Design Canva Figure Drawing
Life Coach Training Neuro-Linguistic Programming Mindfulness Personal Development Personal Transformation Meditation Life Purpose Emotional Intelligence Neuroscience
Web Development JavaScript React CSS Angular PHP WordPress Node.Js Python
Google Flutter Android Development iOS Development Swift React Native Dart Programming Language Mobile Development Kotlin SwiftUI
Digital Marketing Google Ads (Adwords) Social Media Marketing Google Ads (AdWords) Certification Marketing Strategy Internet Marketing YouTube Marketing Email Marketing Retargeting
SQL Microsoft Power BI Tableau Business Analysis Business Intelligence MySQL Data Modeling Data Analysis Big Data
Business Fundamentals Entrepreneurship Fundamentals Online Business Business Strategy Business Plan Startup Freelancing Blogging Home Business
Unity Game Development Fundamentals Unreal Engine C# 3D Game Development C++ 2D Game Development Unreal Engine Blueprints Blender
30-Day Money-Back Guarantee
Development Data Science Python

Cluster Analysis and Unsupervised Machine Learning in Python

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.
Bestseller
Rating: 4.8 out of 54.8 (4,049 ratings)
21,731 students
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], Portuguese [Auto], 
30-Day Money-Back Guarantee

What you'll 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

Course content

8 sections • 56 lectures • 7h 54m total length

  • Preview05:03
  • Course Outline
    04:34
  • What is unsupervised learning used for?
    Preview05:31
  • Preview09:20
  • Where to get the code
    04:36
  • How to Succeed in this Course
    05:51

  • An Easy Introduction to K-Means Clustering
    07:06
  • Hard K-Means: Exercise Prompt 1
    09:13
  • Hard K-Means: Exercise 1 Solution
    11:09
  • Hard K-Means: Exercise Prompt 2
    05:04
  • Hard K-Means: Exercise 2 Solution
    07:08
  • Hard K-Means: Exercise Prompt 3
    06:55
  • Hard K-Means: Exercise 3 Solution
    16:22
  • Hard K-Means Objective: Theory
    13:01
  • Hard K-Means Objective: Code
    05:13
  • Soft K-Means
    05:41
  • The Soft K-Means Objective Function
    01:39
  • Soft K-Means in Python Code
    10:03
  • How to Pace Yourself
    03:19
  • Visualizing Each Step of K-Means
    02:18
  • Examples of where K-Means can fail
    07:32
  • Disadvantages of K-Means Clustering
    02:13
  • How to Evaluate a Clustering (Purity, Davies-Bouldin Index)
    06:33
  • Using K-Means on Real Data: MNIST
    05:00
  • One Way to Choose K
    05:15
  • K-Means Application: Finding Clusters of Related Words
    08:38
  • Clustering for NLP and Computer Vision: Real-World Applications
    06:58
  • Suggestion Box
    03:03

  • Visual Walkthrough of Agglomerative Hierarchical Clustering
    02:35
  • Agglomerative Clustering Options
    03:38
  • Using Hierarchical Clustering in Python and Interpreting the Dendrogram
    04:38
  • Application: Evolution
    14:00
  • Application: Donald Trump vs. Hillary Clinton Tweets
    18:34

  • Gaussian Mixture Model (GMM) Algorithm
    15:31
  • Write a Gaussian Mixture Model in Python Code
    18:54
  • Practical Issues with GMM / Singular Covariance
    09:07
  • Comparison between GMM and K-Means
    03:55
  • Kernel Density Estimation
    06:24
  • GMM vs Bayes Classifier (pt 1)
    09:28
  • GMM vs Bayes Classifier (pt 2)
    11:30
  • Expectation-Maximization (pt 1)
    11:45
  • Expectation-Maximization (pt 2)
    02:24
  • Expectation-Maximization (pt 3)
    08:09
  • Future Unsupervised Learning Algorithms You Will Learn
    01:01

  • Windows-Focused Environment Setup 2018
    20:20
  • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
    17:32

  • How to Code by Yourself (part 1)
    15:54
  • How to Code by Yourself (part 2)
    09:23
  • Proof that using Jupyter Notebook is the same as not using it
    12:29
  • Python 2 vs Python 3
    04:38

  • How to Succeed in this Course (Long Version)
    10:24
  • Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
    22:04
  • Machine Learning and AI Prerequisite Roadmap (pt 1)
    11:18
  • Machine Learning and AI Prerequisite Roadmap (pt 2)
    16:07

  • What is the Appendix?
    02:48
  • Preview05:31

Requirements

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

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

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Team
  • 4.6 Instructor Rating
  • 40,699 Reviews
  • 148,448 Students
  • 14 Courses

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, Postgres, Redis, MongoDB, and more.

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,547 Reviews
  • 423,229 Students
  • 28 Courses

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, Postgres, Redis, MongoDB, and more.

  • Udemy for Business
  • Teach on Udemy
  • Get the app
  • About us
  • Contact us
  • Careers
  • Blog
  • Help and Support
  • Affiliate
  • Impressum Kontakt
  • Terms
  • Privacy policy
  • Cookie settings
  • Sitemap
  • Featured courses
Udemy
© 2021 Udemy, Inc.