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.
- 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.
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
To get started with this repository, follow these steps:
-
Clone the repository:
git clone https://github.com/theprashasst/Introduction-to-Machine-Learning.git cd your-repo-name -
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
-
Open Jupyter Notebook: Start Jupyter Notebook to explore the notebooks and run the code:
jupyter notebook
- Python 3.12 or later
- Jupyter Notebook
- Fast.ai library
- Pandas
- Scikit-learn
- IPython
If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request. Contributions are welcome!
This repository is licensed under the MIT License. See the LICENSE file for more details.
- Special thanks to the Fast.ai team for providing such a comprehensive and accessible course.
- Inspiration and course materials were taken from Fast.ai.