K-Means for Cluster Analysis and Unsupervised Learning

K-Means for Cluster Analysis and Unsupervised Learning, available at $29.99, has an average rating of 3.85, with 8 lectures, based on 44 reviews, and has 2503 subscribers.

You will learn about The basic fundamentals of Unsupervised Learning: Cluster Analysis and Pattern Recognition How the K-Means algorithm works in general. Get an intuitive explanation with graphics that are easy to understand How the K-Means algorithm is defined mathematically and how it is derived. Implementing the K-Means algorithm in Python from scratch. Get a really profound understanding of the working principle How to implement K-Means very fast with one line of code This course is ideal for individuals who are Beginner Python developers curious about data science or Anyone interested in Machine Learning or People who want to get a good start into unsupervised learning or People who want to cluster their data fast It is particularly useful for Beginner Python developers curious about data science or Anyone interested in Machine Learning or People who want to get a good start into unsupervised learning or People who want to cluster their data fast.

Enroll now: K-Means for Cluster Analysis and Unsupervised Learning

Summary

Title: K-Means for Cluster Analysis and Unsupervised Learning

Price: $29.99

Average Rating: 3.85

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: €19.99

Quality Status: approved

Status: Live

What You Will Learn

The basic fundamentals of Unsupervised Learning: Cluster Analysis and Pattern Recognition
How the K-Means algorithm works in general. Get an intuitive explanation with graphics that are easy to understand
How the K-Means algorithm is defined mathematically and how it is derived.
Implementing the K-Means algorithm in Python from scratch. Get a really profound understanding of the working principle
How to implement K-Means very fast with one line of code

Who Should Attend

Beginner Python developers curious about data science
Anyone interested in Machine Learning
People who want to get a good start into unsupervised learning
People who want to cluster their data fast

Target Audiences

Beginner Python developers curious about data science
Anyone interested in Machine Learning
People who want to get a good start into unsupervised learning
People who want to cluster their data fast

Learn why and where K-Means is a powerful tool

Clustering is a very important part of machine learning. Especially unsupervised machine learning is a rising topic in the whole field of artificial intelligence. If we want to learn about cluster analysis, there is no better method to start with, than the k-means algorithm.

Get a good intuition of the algorithm

The K-Means algorithm is explained in detail. We will first cover the principle mechanics without any mathematical formulas, just by visually observing data points and clustering behavior. After that, the mathematical background of the method is explained in detail.

Learn how to implement the algorithm in Python

First we will learn how to implement K-Means from scratch. That means for the beginning no additional packages will be used, except numpy. This is important to get a really good grip on the functioning of the algorithm.

You will of course also learn how to implement the algorithm really quickly by using only one line of code.

The examples will be based on artificial data, which we generate ourselves in the course.

Learn where you should pay attention

K-Means is a powerful tool but it definetely has drawbacks! You will learn where you have to be careful and when you should use the algorithm, and also when it is a bad idea to use the algorithm. I will show you examples and counterexamples on the quality and applicability of this method.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Why K-Means?

Chapter 2: The Mechanics of K-Means

Lecture 1: Visual Explanation

Lecture 2: Mathematical Explanation

Lecture 3: Drawbacks

Chapter 3: Application: Implementation

Lecture 1: Setup of Python, Numpy and Scikit-Learn via Anaconda

Lecture 2: Implementation from scratch using Python and Numpy

Lecture 3: Implementation in one line of code using Scikit-Learn

Chapter 4: Final words

Lecture 1: Final words

Instructors

Hannes Hinrichs
Machine Learning Researcher

Rating Distribution

1 stars: 3 votes
2 stars: 2 votes
3 stars: 8 votes
4 stars: 16 votes
5 stars: 15 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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