K-Means for Cluster Analysis and Unsupervised Learning
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- 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
In this part, I will guide you to have Python with all the neccessary packages installed on Windows 10.
- Basic mathematical skills
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.
- 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