mlcourse: A Jupyter Notebook repository from franperezlopez

Machine Learning Introduction

Install using local Environment

Prerequisites:

Conda installation: Install Anaconda for your OS
After installing Conda, you need to execute following commands from the folder where the repository is located, in order to create a new python environment with all packages needed to run the notebooks:

conda update -n base -c defaults conda
conda env create --name mlcourse -f environment.yml
conda activate mlcourse
python -m ipykernel install --user --name mlcourse --display-name "Python (mlcourse)"
jupyter notebook

Access to the folder data / raw, and uncompress the zip file named bluebook-for-bulldozers.zip, which contains another file named Train.zip that needs to be unzipped to get the file Train.csv

Install using Azure Notebook

Visit the notebook mlcourse in Azure Notebook, and click on the Clone icon to clone the project to your Azure Notebook workspace

Alternatively, you can import this repo to you Azure Notebook account

Once the project is in your workspace, click on the Button Run on Free Computer to start the container; after the container is initializer, you will get the usual Jupyter Notebook screen page.

Execute the notebook

After executing Jupyter server, you can check the notebook located in the folder named notebooks. After opening any notebook, check that the kernel you're using is the one named Python (mlcourse), otherwise change it using the menu Kernel -> Change kernel -> Python (mlcourse).

Bear in mind, executing the Notebook using Azure Notebook is slow as this is a free service. You will get a better performance executing the notebook in your local machine, or executing the Azure Notebook in a server not from the free tier.

Project Organization

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks
│   └── ml1.ipynb      <- Machine Learning Course : Random Forest
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   ├── utils.py       <- Helper methods for handling data and models
│   │
│   ├── data           <- Scripts to download or generate data
│   ├── features       <- Scripts to turn raw data into features for modeling
│   ├── models         <- Scripts to train models and then use trained models to make predictions
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org