K-Means for Cluster Analysis and Unsupervised Learning
The powerful K-Means Clustering Algorithm for Cluster Analysis and Unsupervised Machine Learning in Python
Bullstrack
K-Means for Cluster Analysis and Unsupervised Learning udemy course
The powerful K-Means Clustering Algorithm for Cluster Analysis and Unsupervised Machine Learning in Python
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
Requirements:
- Know how to code in Python and Numpy
- Install Numpy and Scipy
- Matrix arithmetic, probability
Description:
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.
Who this course is for:
- 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
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Course Details:
- 1 hour on-demand video
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
K-Means for Cluster Analysis and Unsupervised Learning udemy free download
The powerful K-Means Clustering Algorithm for Cluster Analysis and Unsupervised Machine Learning in Python
Demo Link: https://www.udemy.com/course/kmeans-clustering/
