Welcome to Apurva's repository for the COMS W4995 Applied Machine Learning (AML) homework! This repository contains various assignments completed by Apurva during the course. Below, you'll find information on how to navigate through the repository, tools used, and how to run the code.
- Python: The primary programming language used for implementing machine learning algorithms and data processing.
- scikit-learn (sklearn): A powerful Python library for machine learning tasks such as classification, regression, clustering, and more.
- Google Colab: A cloud-based Jupyter notebook environment provided by Google, used for running Python code, particularly suitable for machine learning experiments and analysis.
- VS Code (Visual Studio Code): A popular source-code editor developed by Microsoft. It provides excellent support for Python development and version control integration.
The repository is organized as follows:
- Assignment Notebooks: Jupyter notebooks (.ipynb files) containing the code implementations and explanations for each assignment.
- Data Folder: This folder contains any datasets used in the assignments. It may be organized further into subfolders if multiple datasets are used.
- README.md: This markdown file provides an overview of the repository and instructions on how to use it.
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Clone the Repository: You can clone this repository to your local machine using the following command:
git clone https://github.com/Apurva3509/Applied-Machine-Learning.git
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Open in VS Code: Navigate to the cloned repository directory using VS Code. You can use VS Code's built-in terminal for running Python scripts and managing the repository.
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Run the Notebooks: Open the assignment notebooks (.ipynb files) using Jupyter or Google Colab. Follow the instructions within the notebooks to execute the code cells and understand the solutions provided by Apurva.
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Explore Data and Results: Feel free to explore the datasets in the 'Data' folder and analyze the results obtained from running the code in the notebooks.
--If you have any questions or suggestions regarding this repository, feel free to reach out to Apurva via email at apurva.patel@columbia.edu
Thank you for visiting my COMS W4995 AML Homework Repository! Happy learning! 🚀📚