English | Size: 476 MB
Genre: eLearning
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
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