Deep learning starting….

To start with deep learning:

Intuitive and practical….. PCA, SVD, dimensionality reduction and all that…..

Links to places than explain well how to do PCA and how to understand it…….

Haz clic para acceder a principal_components.pdf

Using Python for data analysis, machine learning

Here is an excellent list of tools from python that you can use for your machine learning projects:

extracted from this reference:

  • NumPy/Scipy You probably know about these already. But let me point out the Cookbook where you can read about many statistical facilities already available and the Example List which is a great reference for functions (including data manipulation and other operations). Another handy reference is John Cook’s Distributions in Scipy.
  • pandas This is a really nice library for working with statistical data — tabular data, time series, panel data. Includes many builtin functions for data summaries, grouping/aggregation, pivoting. Also has a statistics/econometrics library.
  • larry Labeled array that plays nice with NumPy. Provides statistical functions not present in NumPy and good for data manipulation.
  • python-statlib A fairly recent effort which combined a number of scattered statistics libraries. Useful for basic and descriptive statistics if you’re not using NumPy or pandas.
  • statsmodels Statistical modeling: Linear models, GLMs, among others.
  • scikits Statistical and scientific computing packages — notably smoothing, optimization and machine learning.
  • PyMC For your Bayesian/MCMC/hierarchical modeling needs. Highly recommended.
  • PyMix Mixture models.

If speed becomes a problem, consider Theano — used with good success by the deep learning people.

Information for the tools:

For Pandas:

for a short summary on pandas:

For Numpy/Scipy:

For GLM:

Monte Carlo

Courses Machine learning

Excellent follow up courses on machine learning  … Carnegie Mellon, Tom Mitchell

.. Andrew Ng  CS 229 , it is diferent and more advanced than the coursera course by Ng

Machine Learning by Pedro Gomingos, University of Wasington