This repository contains code and resources for learning machine learning through a free course available on Kaggle. The course covers various topics in machine learning, including data preprocessing, model training, evaluation, and deployment.
The Kaggle course provides a comprehensive introduction to machine learning concepts and techniques. It includes hands-on coding exercises, real-world datasets, and practical examples to help you grasp key concepts effectively. Throughout the course, you'll learn:
- Basics of Python programming for machine learning
- Data preprocessing techniques such as data cleaning, feature scaling, and feature engineering
- Supervised learning algorithms including linear regression, logistic regression, decision trees, and random forests
- Unsupervised learning techniques like clustering and dimensionality reduction
- Model evaluation and validation methods
- Techniques for improving model performance
- Introduction to deep learning and neural networks
- notebooks/: Contains Jupyter notebooks for each lesson or exercise in the course.
- datasets/: Stores datasets used in the exercises and examples.
- resources/: Additional resources such as slides, PDFs, or supplementary materials provided in the course.
To get started with this repository, follow these steps:
- Clone the repository to your local machine:
git clone https://github.com/on3ss/kaggle-learn
- Navigate to the repository directory:
cd kaggle-learn
- Explore the
notebooksdirectory to access Jupyter notebooks for each lesson or exercise.
- Python 3.x
- Jupyter Notebook
- Required libraries (NumPy, Pandas, Matplotlib, Scikit-learn, etc.)
You can install the required libraries using pip:
pip install numpy pandas matplotlib scikit-learn jupyter
Contributions to this repository are welcome. If you find any errors, have suggestions for improvements, or want to add additional resources, feel free to open an issue or submit a pull request.
This repository is licensed under the MIT License. See the LICENSE file for details.
Special thanks to Kaggle for providing free educational resources and courses for aspiring data scientists and machine learning enthusiasts.
Happy Learning! 🚀🤖