PCA For Dimension Reduction And Visualization, Temperature-Yield Prediction Via Linear Regression, And Linear Fit Optimization Using Gradient Descent.

This repository consists of Lab Assignments for course Machine Learning.

Machine Learning / Gradient Descent

This repository consists of Lab Assignments for course Machine Learning for Data Mining.

Solved tasks of "Machine Learning" course, contains implementations of main machine learning algorithms.

Implementing the gradient descent algorithm from scratch to perform univariate linear regression to analyze the profit made by a bike sharing company.

Python code for visualizing Gradient Descent optimization paths with animated contours. Demonstrates two strategies: fixed and optimal step sizes. Includes Fibonacci search for step size and data saved with Pickle.

Gradient Descent is the process of minimizing a function by following the gradients of the cost function. This involves knowing the form of the cost as well as the derivative so that from a given point you know the gradient and can move in that direction, e.g. downhill towards the minimum value.

An easy implementation of the GD implementation, comparison using different functions using fixed alpha or the alpha obtained through backtracking algo

Applying Gradient Descent from scratch and analyzing the effect of changing the number of epochs and learning rate on the mean square error (MSE).

Is there a linear connection between immigration and unemployment rate? Let's implement LR and check!

Created following @StephenGrider Udemy's course

Gradient Descent implementation for Multiple linear regression

A comparison of Linear Regression implemented from scratch using Gradient Descent in Python versus Scikit-learn’s built-in LinearRegression model. Includes training, evaluation using Mean Squared Error, and insights into how gradient descent compares with the closed-form solution.

About my Machine Learning Class Assignments.