This post is a great summary of the bias versus varianve dilemma and how to solve it
This information came from a post of Santiago Egea (Universidad de Valladolid)
To start with deep learning:
Links to places than explain well how to do PCA and how to understand it…….
New edition coming of:
Mining Massive Datasets from Stanford
On this month starts a new edition of the Statistical Learning course in Stanford on-line:
and excellent course from Hastie and Tibshirani. The course is based on R.
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 a short summary on pandas: