Projectpro – Build Regression (Linear,Ridge,Lasso) Models in NumPy Python 2024-7

Descriptions

Build Regression (Linear,Ridge,Lasso) Models in NumPy Python, Regression is a supervised learning algorithm. Regression analysis is used to establish a relationship between one or more independent variables and a dependent variable. There are several variations in regression analysis like linear, multiple linear, and nonlinear. One can create regression models with the help of the ‘Scikit-learn’ library, the most valuable and robust library for machine learning in Python. But, in this project, we will be building our models from scratch using NumPy. Building your model allows for more flexibility during the training process, and one can tweak the model to make it more robust and responsive to real-world data as required in the future during re-training or in production. This project explains how linear regression works and how to build various regression models such as linear regression, ridge regression, lasso regression, and decision tree from scratch using the NumPy module.

What you’ll learn

• What is Regression
• What are the applications of regression
• Different types of regression
• Differentiation between regression and classification
• What is linear regression
• What is loss function
• What is gradient descent
• Drawbacks of linear regression
• Understanding bias and variance

Specificatoin of Build Regression (Linear,Ridge,Lasso) Models in NumPy Python

• Publisher : Projectpro
• Teacher : Projectpro
• Language : English
• Level : All Levels
• Number of Course : 13
• Duration : 2 hours and 31 minutes

Content of Build Regression (Linear,Ridge,Lasso) Models in NumPy Python

Requirements

• Language – Python
• Libraries – Pandas, NumPy

Pictures

Sample Clip

Installation Guide

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Subtitle : English

Quality: 1080p

Download Links

Download – 476 MB

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File size

476 MB