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Merge pull request #18 from gjbex/development
Fix for changes in TensorFlow API
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‎README.md

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# Python for machine learning
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# Machine learning with Python
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GitHub repository for participants of the "Python for machine learning" training.
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GitHub repository for participants of the "Machine learning with Python" training.
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For information on the training, see the website
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[https://gjbex.github.io/Python-for-data-science/](https://gjbex.github.io/Python-for-machine-learning/)
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[https://gjbex.github.io/Machine-learning-with-Python/](https://gjbex.github.io/Machine-learning-with-Python/)
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## What is it?
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1. [`python_for_machine_learning.pptx`](python_for_machine_learning.pptx): PowerPoint
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1. [`machine_learning_with_python.pptx`](machine_learning_with_python.pptx): PowerPoint
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presentation used for the training.
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1. [`hands-on`](hands-on): Jupyter notebooks for hands-on sessions.
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1. [`source-code`](source-code): sample code written to develop the slides and
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illustrate concepts.
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1. [`environment.yml`](environment.yml): conda environment file intended to be
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cross-platform.
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1. [`python_for_machine_learning_linux64_conda_specs.txt`](python_for_machine_learning_linux64_conda_specs.txt):
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1. [`machine_learning_with_python_linux64_conda_specs.txt`](machine_learning_with_python_linux64_conda_specs.txt):
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conda environment specification file specific for 64-bit Linux to precisely
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reproduce the environment on which the code was developed.
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1. [License](LICENSE): license information for the material in this repository.
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## Video sessions
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Video recordings of this training are available on YouTube.
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Video recordings of this training are available on YouTube. **Note:** the
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recordings were made in 2020 and have not been updated since.
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1. [Introduction](https://youtu.be/QIZ0-oHwMaI) (25 minutes)
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1. [scikit-learn: data pipelines and regression](https://youtu.be/sy4U9VteP8Q) (28 minutes)

‎docs/README.md

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The Python programming language is increasingly popular. It is a
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versatile language for general purpose programming and accessible
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for novice programmers. However, it is also the de facto go-to
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language for machine learning applications. This training
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introduces modules that are useful in that context.
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Machine learning plays an increasingly important role in many scientific
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domains. A Python programmer can use high-quality machine learning
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frameworks for classic machine learning as well as for state-of-the-art
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algorithms.
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This training concentrates on methodological and practical aspects of
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machine learning and how to apply those using Python.
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## Learning outcomes
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## Training materials
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Slides are available in the
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[GitHub repository](https://github.com/gjbex/Python-for-machine-learning),
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[GitHub repository](https://github.com/gjbex/Machine-learning-with-Python),
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as well as example code and hands-on material.
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‎environment.yml

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name: python_for_machine_learning
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name: machine_learning_with_python
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channels:
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- conda-forge
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dependencies:
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- hyperopt
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- bayesian-optimization
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- gplearn
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prefix: /home/gjb/mambaforge/envs/tensorflow
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prefix: /home/gjb/mambaforge/envs/machine_learning_with_python

‎python_for_machine_learning.pptx renamed to ‎machine_learning_with_python.pptx

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‎machine_learning_with_python_linux64_conda_specs.txt

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# This file may be used to create an environment using:
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# $ conda create --name <env> --file <this file>
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# platform: linux-64
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@EXPLICIT
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https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
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https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda
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