Logistic Regression is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. In logistic regression, the dependent variable is a binary variable that contains data

Ridge Regression Ridge regression learns , using the same least-squares criterion but adds a penalty for large variations in parameters. The addition of a penalty parameter is called regularization. Regularization is an important concept

Before writing the next post about Algorithms, I thought it was important to talk first about feature normalization, as it will be relevant in almost all algorithms moving forward. Some of the algorithms

A linear model is a sum of weighted variables that predict a target output value given an input data instance. For example: car prices. A car has different features like: year built, horse

In our previous post about Algorithms, we talked about K Neighbors Classification Model, however this algorithm can be used for regression as well. In a regression problem you have datasets, let's say with

According to Microsoft: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py An Environment defines Python packages, environment variables, and Docker settings that are used in machine learning