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Introduction to Machine Learning with Fast.ai

Welcome to the Introduction to Machine Learning with Fast.ai repository! This repository is dedicated to my journey through the Fast.ai course, where I will be learning and implementing various machine learning techniques using the Fast.ai library. This repository will serve as a collection of my notes, code exercises, and project implementations as I progress through the course.

Repository Structure

  • Notebooks: This directory contains Jupyter notebooks for each lesson, complete with code, explanations, and exercises.
  • Datasets: Sample datasets used for practice and projects. (Note: Large datasets might be linked or referenced instead of stored directly in the repository.)
  • Projects: End-to-end projects applying the concepts learned in the course to real-world datasets.
  • Notes: Detailed notes and summaries of key concepts from each lesson.
  • Scripts: Python scripts for various utility functions and experiments.

Course Overview

The Fast.ai course is designed to provide a practical, hands-on introduction to machine learning. It covers a wide range of topics, including:

  • Introduction to machine learning concepts
  • Data preprocessing and visualization
  • Model training and evaluation
  • Feature engineering
  • Advanced techniques like ensembling and model interpretation

Getting Started

To get started with this repository, follow these steps:

  1. Clone the repository:

    git clone https://github.com/theprashasst/Introduction-to-Machine-Learning.git
cd your-repo-name

  2. Install the required packages: Ensure you have Python 3.12 installed and run the following command to install the necessary packages:

    pip install -r requirements.txt

  3. Open Jupyter Notebook: Start Jupyter Notebook to explore the notebooks and run the code:

    jupyter notebook

Requirements

  • Python 3.12 or later
  • Jupyter Notebook
  • Fast.ai library
  • Pandas
  • Scikit-learn
  • IPython

Contributing

If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request. Contributions are welcome!

License

This repository is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

  • Special thanks to the Fast.ai team for providing such a comprehensive and accessible course.
  • Inspiration and course materials were taken from Fast.ai.