Pattern Recognition ResourceS

More data beats clever algorithms, but better data beats more data.   

―  Peter Norvig

1. Starting with Python for Machine Learning (I)

   1. Theory

   2. Practice (notebook . ipynb)

2. Elements of Probability Theory and Bayesian Estimation

   1. Theory

   2. Practice (notebook . ipynb)

3. Underfitting, Overfitting, Bias–Variance trade-off and the Hoccam Razor Criterion

   1. Theory

   2. Practice (notebook .ipynb)