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100JoursDeML

★ 90 Open GitHub ↗

Repertoire pour tous les codes du challenge 100 jours de machine learning #100JoursDeML

// repository documentation

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# 100JoursDeML

**100JoursDeML** is a personal challenge to explore machine learning topics over
one hundred days. The repository collects the notebooks and slides used during
the journey. Most of the material is in French and follows the companion
[YouTube playlist](https://www.youtube.com/watch?v=SwGghwhk9Ak&list=PLyh35eYRez8e0KxsArawcDm6TYPVq0rU_&index=1).

## Directory overview

| Directory | Topics covered |
|-----------|----------------|
| `01_Debuter_En_Python` | Getting started with Python, NumPy, Pandas and basic visualization |
| `02_Statistiques_Pour_Le_Machine_Learning` | Statistics fundamentals for machine learning |
| `03_Preprocessing` | Data cleaning, missing value treatment and normalization |
| `04_Selection_Features` | Manual and automatic feature selection techniques |
| `05_Apprentissage_Supervise` | Supervised learning algorithms and model validation |
| `06_Apprentissage_Non_Supervisé` | Clustering and other unsupervised methods |
| `07_Series_Temporelles` | Time series analysis including ARIMA, VAR, VECM and GARCH |
| `08_NLP` | Introduction to natural language processing and sentiment analysis |

## Running the notebooks

1. Install Python 3.8 or later.
2. Install commonly used packages:

```bash
   pip install numpy pandas matplotlib seaborn scikit-learn statsmodels nltk jupyter
   ```

3. Launch Jupyter from the repository root:

```bash
   jupyter notebook
   ```

Open the notebook you wish to run from the browser interface.

## License

No license file is provided at the moment. If you wish to reuse the content,
please contact the repository maintainers for clarification.

## Contributing

Contributions are welcome via pull requests. Feel free to submit fixes or
improvements.

100JoursDeML

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