/Project-Linear-Regression-with-Numpy

Performing coding for Linear Regression using simple python (and its libraries) without use of specialized (Scikit learn, Tensorflow, Keras, Pytorch) libraries to learn.

Primary LanguageJupyter Notebook

Project-Linear-Regression-with-Numpy

This repo is about understanding linear regression algorithm by using Numpy. Although linear regression can be done by other python libraries in most simplest ways, but this is my attempt for practicing and learning basics of this with numpy lib.

Covering with Step-wise python coding in jupyter notebook.

#1 Load the Data and Libraries

#2 Visualize the Data

#3 Compute the Cost 𝐽(𝜃)

#4 Gradient Descent

#5 Visualising the Cost Function 𝐽(𝜃)

#6 Plotting the Convergence

#7 Training Data with Linear Regression Fit

#8 Inference using the optimized 𝜃 values

Reference: This project is a guided project from Coursera https://www.coursera.org/learn/linear-regression-numpy-python