ml-nb: Machine Learning Notebook

This repository contains a set of IPython Notebooks about machine learning algorithms and its applications. Some of the content is based on the PRML Book by Christopher Bishop.

Requirements:

The notebooks are developed with:

• Python 3
• Numpy
• SciPy
• Pandas

These libraries can be easily installed using Anaconda and Pip.

Notebooks

1. Parametric Density Estimation: Estimating a parametric density distributions.
2. Nonparametric Density Estimation: How about non-parametric density estimation.
3. Linear Regression Models: Classic linear models for regression in a toy data.
4. Linear Classification Models: Linear classification models applied to multi-class classification.
5. Neural Networks: Exploring non-linearity with Neural networks.
6. Kernel Trick: Applying the kernel trink on regression models.