A list of machine learning tasks carried out in a set of series spread across 3 Colab Notebooks
This repository contains a set of machine learning tasks performed in a series of three Colab notebooks. The notebooks cover various aspects of machine learning, including data preprocessing, model selection, training, evaluation, and deployment.
The format of this project is tasks carried out part by part in the notebooks. You will observe a 'question and answer' shape throughout the notebooks.
- Python 3.x
- Jupyter Notebook or Google Colab
- Clone the repository:
git clone https://github.com/your-username/Machine_Learning-_Tasks.git - Open each notebook in Jupyter Notebook or Google Colab.
- Customize the code as needed, such as updating file paths, model configurations, or hyperparameters.
- Follow the instructions in each notebook to execute the code cells sequentially.
- Analyze the results, evaluate the model performance, and explore the deployed model.
Contributions are welcome! If you encounter any issues or have suggestions for improvement, please feel free to submit a pull request or open an issue.
This project is licensed under the MIT License.
This project was inspired by the desire to provide a comprehensive overview of machine learning tasks and their practical implementation. I appreciate the contributions of the open-source community and various libraries used in this project.