Implementation of different machine learning algorithms written in Python.
pip install -r requirements.txt
NOTE: scikit-learn module is used only for accessing the datasets and scalers.
python run_{algorithmToRun}.py
NOTE: All scripts have additional command arguments that can be given by the user.
python run_{algorithmToRun}.py --help
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.
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.
This was achieved by adding polynomial features.
This was achieved by adding polynomial features.
Link to first Reddit post
Link to second Reddit post
Sentdex: ML from scratch
Coursera Andrew NG: Machine Learning
- SVM classification, gaussian kernel
- Mean Shift
- PCA
- DecisionTree
- Neural Network