https://github.com/jaidevd/talentsprint-workshop

TalentSprint workshop on Machine Learning in November 2017
https://github.com/jaidevd/talentsprint-workshop

data-science jupyter-notebook machine-learning python sklearn tutorial

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TalentSprint workshop on Machine Learning in November 2017

• Host: GitHub
• URL: https://github.com/jaidevd/talentsprint-workshop
• Owner: jaidevd
• License: bsd-3-clause
• Created: 2017-11-14T14:09:42.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2017-11-17T10:52:54.000Z (about 7 years ago)
• Last Synced: 2024-10-16T04:22:34.437Z (3 months ago)
• Topics: data-science, jupyter-notebook, machine-learning, python, sklearn, tutorial
• Language: Jupyter Notebook
• Homepage:
• Size: 581 KB
• Stars: 1
• Watchers: 4
• Forks: 30
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
Here's a brief plan of the four sessions of the workshop. Each of these

sections will include exercises based on real-world datasets. While most of the

workshop depends only on scikit-learn, there are a few other requirements too.

An exhaustive list of Python packages required for the workshop is as follows.

Requirements:

-------------

- NumPy

- SciPy

- Matplotlib

- Pandas

- scikit-learn

- tensorflow

- keras

- theano

At most a couple more cursory packages might get added to this list as I

proceed with creating the material, but those should be easily installable at

the venue itself, assuming that the participants have a Python distribution

like Enthought Canopy or Anaconda installed.

    

Saturday Pre-Lunch

------------------

* Inbuilt dataset loading utilities

* Introduction to the estimator object

* Basic classification & regression tasks

* Introduction to supervised and unsupervised learning

Saturday Post-Lunch

-------------------

* Linear vs Nonlinear models

* Kernel Methods in Machine Learning

* Feature selection & Dimensionality Reduction

* Interpreting a trained model

Sunday Pre-Lunch

----------------

* Measuring model performance

* Cross validation

* Grid and random parameter search

Sunday Post-Lunch

-----------------

* Gradient descent and its variations

* Introduction to neural networks

* Building a shallow neural network

* Brief introduction to deep learning