Machine Learning Algorithms

This repository contains my implementations of fundamental machine learning algorithms using Python and NumPy. The notebooks were developed while learning machine learning and cover several core supervised learning algorithms and neural network architectures.

Algorithms Included

• Linear Regression
• Polynomial Regression
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Neural Network for Binary Classification
• Neural Network with Softmax Output

Technologies Used

• Python
• NumPy
• Pandas
• Matplotlib

Installation

Clone the repository:

git clone https://github.com/pranavishvar/Machine-Learning-WOC.git

Install the required dependencies:

pip install numpy pandas matplotlib

Open any notebook in Google Colab or your preferred Python environment and run the cells.

Repository Structure

Machine-Learning-WOC/
├── ML_algorithm_library.ipynb
├── Neural Network for binary classification.ipynb
├── Neural_Network_softmax.ipynb
├── knn_test_predictions.csv
├── linear_regression_test_predictions.csv
├── logistic_regression_test_predictions.csv
├── nn_test_predictions_binary_labels.csv
└── polynomial_regression_test_predictions.csv

References

• Andrew Ng, Machine Learning Specialization (Coursera)