Machine Learning Algorithms from Start to Finish in Python: Linear Regression

Machine Learning Algorithms from Start to Finish in Python: Linear Regression

Machine Learning Algorithms from Start to Finish in Python: Linear Regression. Probably one of the most common algorithms around, Linear Regression is a must know for Machine Learning Practitioners.

Probably one of the most common algorithms around, Linear Regression is a must know for Machine Learning Practitioners. This is usually a beginner’s first exposure to a real Machine Learning algorithm, and knowing how it operates on a deeper level is crucial to gain a better understanding of it.

So, briefly, let’s break down the real question; What really is Linear Regression?

Linear Regression Definition

Linear Regression is a supervised learning algorithm that aims at taking a linear approach at modelling the relation between a dependent variable and an independent variable. In other words, It aims to fit a linear trendline _that best captures the _relationship of the data, and, from this line, it can predict what the target values may be.

Great, I know the definition, but how does it really work? Great question! In order to answer the question, let’s run through a step by step process of how Linear Regression really operates:

  1. A trendline is fit to the data(as illustrated above).
  2. The distance between the points(the red dots on the figure being the points, and the green line being the distance) is calculated, and then squared, before they are summed(The values are squared to ensure that negative values do not produce an incorrect value and hinder the calculation). This is the error of the algorithm, or better knows as the residual
  3. The residual for the iteration is stored
  4. Based on an _optimisation algorithm, _thelineis slightly “shifted” so that the line may fit the data better.
  5. Steps 2–5 are repeated until a desirable result is reached, or the residual error has decreased to zero.

This method of fitting a line is known as Least Squares.

data-visualization algorithms machine-learning artificial-intelligence python

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

AI(Artificial Intelligence): The Business Benefits of Machine Learning

Enroll now at CETPA, the best Institute in India for Artificial Intelligence Online Training Course and Certification for students & working professionals & avail 50% instant discount.

3 Ways to Select Features Using Machine Learning Algorithms in Python

3 Ways to Select Features Using Machine Learning Algorithms in Python. In this article, take a look at three ways to select features using machine learning learning algorithms in Python.

Pipelines in Machine Learning | Data Science | Machine Learning | Python

Machine Learning Pipelines performs a complete workflow with an ordered sequence of the process involved in a Machine Learning task. The Pipelines can also

AutoML: Automated Machine Learning | Data Science | Machine Learning | Python

AutoML makes the power of a Machine Learning algorithm available to you even if you don't have the complete knowledge of Machine Learning.You can use AutoML

Contact Tracing with Machine Learning | Data Science | Machine Learning | Python

I will be proposing a contact tracing algorithm that relies on GPS data, which can be used in contact tracing with machine learning.