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Python for machine learning

GitHub repository for participants of the "Python for machine learning" training. For information on the training, see the website https://gjbex.github.io/Python-for-data-science/

What is it?

  1. python_for_machine_learning.pptx: PowerPoint presentation used for the training.
  2. hands-on: Jupyter notebooks for hands-on sessions.
  3. source-code: sample code written to develop the slides and illustrate concepts.
  4. environment.yml: conda environment file intended to be cross-platform.
  5. python_for_machine_learning_linux64_conda_specs.txt: conda environment specification file specific for 64-bit Linux to precisely reproduce the environment on which the code was developed.
  6. License: license information for the material in this repository.
  7. Contributing: information on how to contribute to this repository.
  8. docs: directory containing the website for this repository.

Video sessions

Video recordings of this training are available on YouTube.

  1. Introduction (25 minutes)
  2. scikit-learn: data pipelines and regression (28 minutes)
  3. scikit-learn: classification and clustering (12 minutes)
  4. keras: introduction to neural networks (13 minutes)
  5. keras: multilayer perceptrons for digit recognition (34 minutes)
  6. [keras: convolutional neural networks for digit recognition] (19 minutes)
  7. keras: recurrent neural networks for sentiment classification (26 minutes)