SQL | Alias Operator
SQL Alias Syntax for Columns: Also read Alias Example Assume we have a table called “Persons” and another table called “Product_Orders”. We will give the table aliases of “p” and…
SQL | The LIKE Operator
SELECT column_name(s) FROM table_name WHERE column_name LIKE pattern LIKE Operator Example The “Persons” table: Now we want to select the persons living in a city that starts with “s” from…
Understanding DBSCAN Clustering Algorithm: Implementation in Python
Before we move on to Gaussian mixture models, let’s take a look at DBSCAN, another popular clustering algorithm that illustrates a very different approach based on local density estimation. This…
Using Clustering for Semi-Supervised Learning
Another use case for clustering is in semi-supervised learning, when we have plenty of unlabeled instances and very few labeled instances. Let’s train a logistic regression model on a sample…
Using clustering for image segmentation
Image segmentation is the task of partitioning an image into multiple segments. In semantic segmentation, all pixels that are part of the same object type get assigned to the same…
Accelerated K-Means and Mini-batch K-Means
Introduction Another important improvement to the K-Means algorithm was proposed in a 2003 paper by Charles Elkan. It considerably accelerates the algorithm by avoiding many unnecessary distance calculations: this is…
Implementation of K-Means Clustering in Machine Learning
Consider the unlabeled dataset represented in Figure below: you can clearly see 5 blobs of instances. The K-Means algorithm is a simple algorithm capable of clustering this kind of dataset…
What is Clustering? A Simple Approach
As you enjoy a hike in the mountains, you stumble upon a plant you have never seen before. You look around and you notice a few more. They are not…
What is Stacking of Models in Machine Learning?
The last Ensemble method we will discuss in this series is called stacking (short for stacked generalization). It is based on a simple idea: instead of using trivial functions (such…
Locally linear Embedding For Dimensionality Reduction in Machine Learning
Locally Linear Embedding (LLE) is another very powerful nonlinear dimensionality reduction (NLDR) technique. It is a Manifold Learning technique that does not rely on projections like the previous algorithms. In…