Understanding Machine Learning Algorithms In Python

Machine learning algorithms play a critical role in delivering engaging user experiences by accurately analyzing user behaviour through historical data. As of today, an increasing number of enterprises rely on ML-based applications to monitor their customer behaviour and identify consumer preferences. The machine learning data enables them to formulate effective trade strategies, in-line with the preferences and expectations of their potential customers. That being said, the demand for machine learning app development is rapidly increasing to fulfil the dynamically changing enterprise requirements. Nevertheless, a majority of businesses seek Python app development services for implementing machine learning algorithms.

At Oodles Technologies, we have gained extensive experience in Python-based machine learning application development. Our development team is skilled at using the latest Python frameworks and agile methodologies to develop high-performance machine learning applications. Based on our past experience in Python app development, we have listed down the five most effective ML algorithms in Python.

Also read Using AI with AR and VR To Enhance Customer Experience

Popular Machine Learning Algorithms In Python

Below are the most popular machine learning algorithms used in Python.

Linear Regression

It is the most fundamental and yet the most significant machine learning algorithm that every data scientist must know. Linear regression lays the groundwork for all the complex ML algorithms and thus, is the most significant of all. In technical terms, linear regression is a statistical technique used to establish a relationship between a dependent variable and a set of independent variables. In Python, linear regression is mainly divided into two categories:


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