deep_ml_curriculum

First run of Data science and Machine Learning training for Oil and Gas.

Teaches DS&ML using some Oil and Gas specific datasets and examples. Some of these are well log facies prediction, seismic interpretation, geospatial plotting, production plotting, and more.

See the Three Springs Technology product page for more information or to schedule this course email [email protected].

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Course notebooks. a is reserved for a python course. b is data science, c is machine learning
│   ├── b01_SQL        <- Start of Couese b
│   ├── b02_Advanced_Pandas
│   ├── b03_Data_Visualisation
│   ├── b04_DS_Basics
│   ├── b05_Supervised_Learning
│   ├── b06_Evaluation_Metrics
│   ├── b07_Selfsupervised
│   ├── b08_Interactive_Plotting
│   ├── b09_Time_Series_Analysis
│   ├── b10_Time_Series_Forecasting
│   ├── b11_Geopandas
│   ├── b12_Final_Project
│   ├── c01_Intro_to_NN_Part_1
│   ├── c02_Intro_to_NN_Part_2
│   ├── c03_Finetuning
│   ├── c04_Tabular_Data
│   ├── c05_Big_Data
│   ├── c06_Hyperparameter_Optimization
│   ├── c07_Recurrent_Neural_Networks
│   ├── c08_Object_Detection
│   ├── c09_Autoencoders
│   ├── c10_GANs
│   ├── c11_Final_Project
│   └── z00_Data_prep  <- Preperation of datasets
│
├── requirements       <- The requirements files for reproducing the analysis environment, e.g.
│                         generated with `make doc_reqs`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── deep_ml_curriculum <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py

Setup the data

The data is stored on a public (read only) s3 bucket

git clone https://github.com/3springs/deep_ml_curriculum.git
cd <project root>
# install packages in conda
conda env update --file requirements/environment.min.yml
# install the module, as an editable pip module
pip install -e .
# pull raw the data from public s3 bucket (~10Gb)
aws s3 sync s3://deep-ml-curriculum-data/data/processed/ ~/notebooks/deep_ml_curriculum/data/processed/ --region ap-southeast-2 --no-sign-request

Setup the environment

See ./requirements/readme.md

Contributors ✨

Thanks goes to these wonderful people (emoji key):

_Camilo
💻

_Pooya
💻

_{Mike C}
💻

_{Sean Driver}
📆

_the-winter
📆

Made by

Credits

Many of the datasets or notebooks are based on resources that were generously made open source by the authors. These are aknowledged either in a readme file associated with the data, in the notebook, or at the end of the notebook.

Project based on the cookiecutter data science project template. #cookiecutterdatascience

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