Machine Learning Algorithms
Implementation of different machine learning algorithms written in Python.
Installation of libraries
pip install -r requirements.txt
NOTE: scikit-learn module is used only for accessing the datasets and scalers.
Usage
python run_{algorithmToRun}.py
NOTE: All scripts have additional command arguments that can be given by the user.
python run_{algorithmToRun}.py --help
Summary
This project was initially started to help understand the math and intuition behind different ML algorithms, and why they work or don't work, for a given dataset. I started it with just implementing different versions of gradient descent for Linear Regression. I also wanted to visualize the training process, to get a better intuition of what exactly happens during the training process. Over the course of time, more algorithms and visualizations have been added.
Algorithms and Visualizations
Gradient Descent 2D
Gradient Descent 3D
Gradient Descent with LARGE Momentum 2D
Gradient Descent with LARGE Momentum 3D
NOTE: Large value of momentum has been used to exaggerate the effect of momentum in gradient descent, for visualization purposes. The default value of momentum is set to 0.3, however 0.75 and 0.8 was used in the visualization for 2D and 3D respectively.
Linear Regression
Linear Regression for a non-linear dataset
This was achieved by adding polynomial features.
Logistic Regression
Logistic Regression for a non-linear dataset
This was achieved by adding polynomial features.
K Nearest Neighbors 2D
K Nearest Neighbors 3D
KMeans 2D
KMeans 3D
