Performance evaluation is the most important part of machine learning in my opinion. Because machine learning itself has become pretty easy because of all the libraries and packages. Anyone can…
Feature selection is one of the most important parts of machine learning. In most datasets in the real world, there might be many features. But not all the features are…
Stochastic gradient descent is a widely used approach in machine learning and deep learning. This article explains stochastic gradient descent using a single perceptron, using the famous iris dataset. I…
One of the most basic, popular, and powerful statistical models is logistic regression. If you are familiar with linear regression, logistic regression is built upon linear regression. It uses the…
Logistic regression is very popular in machine learning and statistics. It can work on both binary and multiclass classification very well. I wrote tutorials on both binary and multiclass classification…
Nowadays, we see recommendation systems everywhere. When you buy something in an online marketplace like Amazon, eBay, or any other place, they suggest similar products. On Netflix or youtube, you…
Anomaly detection can be treated as a statistical task as an outlier analysis. But if we develop a machine learning model, it can be automated and as usual, can save…
Gaussian distribution is the most important probability distribution in statistics and it is also important in machine learning. Because a lot of natural phenomena such as the height of a…
Developing an efficient machine learning algorithm with a skewed dataset can be tricky. For example, the dataset is about fraudulent activities in the bank or cancer detection. What happens is…
This is a trendy topic. Machine learning is the fastest-growing field today. The job market is skyrocketing. More and more universities are coming up with new courses. So many free…