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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.

• 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.

• Inbuilt dataset loading utilities
• Introduction to the estimator object
• Basic classification & regression tasks
• Introduction to supervised and unsupervised learning

• Linear vs Nonlinear models
• Kernel Methods in Machine Learning
• Feature selection & Dimensionality Reduction
• Interpreting a trained model

• Measuring model performance
• Cross validation
• Grid and random parameter search

• Gradient descent and its variations
• Introduction to neural networks
• Building a shallow neural network
• Brief introduction to deep learning