https://github.com/gjbex/machine-learning-with-python

Repository for participants of the "Machine learning with Python" training
https://github.com/gjbex/machine-learning-with-python

deep-learning keras machine-learning python python-training scikit-learn training

Last synced: 2 months ago
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Repository for participants of the "Machine learning with Python" training

• Host: GitHub
• URL: https://github.com/gjbex/machine-learning-with-python
• Owner: gjbex
• License: cc-by-4.0
• Created: 2020-01-15T13:07:10.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-04-25T13:18:50.000Z (9 months ago)
• Last Synced: 2024-11-22T04:42:45.473Z (2 months ago)
• Topics: deep-learning, keras, machine-learning, python, python-training, scikit-learn, training
• Language: Jupyter Notebook
• Homepage: https://gjbex.github.io/Machine-learning-with-Python/
• Size: 11.6 MB
• Stars: 14
• Watchers: 5
• Forks: 25
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# 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/](https://gjbex.github.io/Python-for-machine-learning/)

## What is it?

1. [`python_for_machine_learning.pptx`](python_for_machine_learning.pptx): PowerPoint

   presentation used for the training.

1. [`hands-on`](hands-on): Jupyter notebooks for hands-on sessions.

1. [`source-code`](source-code): sample code written to develop the slides and

   illustrate concepts.

1. [`environment.yml`](environment.yml): conda environment file intended to be

   cross-platform.

1. [`python_for_machine_learning_linux64_conda_specs.txt`](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.

1. [License](LICENSE): license information for the material in this repository.

1. [Contributing](CONTRIBUTING.md): information on how to contribute to this

   repository.

1. docs: directory containing the website for this repository.

## Video sessions

Video recordings of this training are available on YouTube.

1. [Introduction](https://youtu.be/QIZ0-oHwMaI) (25 minutes)

1. [scikit-learn: data pipelines and regression](https://youtu.be/sy4U9VteP8Q) (28 minutes)

1. [scikit-learn: classification and clustering](https://youtu.be/acXmk4Bx8pI) (12 minutes)

1. [keras: introduction to neural networks](https://youtu.be/-CO0Y8wzYeI) (13 minutes)

1. [keras: multilayer perceptrons for digit recognition](https://youtu.be/nAixWMYgzdo) (34 minutes)

1. [keras: convolutional neural networks for digit recognition] (19 minutes)

1. [keras: recurrent neural networks for sentiment classification](https://youtu.be/TkafYl9APpM) (26 minutes)