For information on how works AUC and ROC in detail:
this paper from HP laboratories is simply incredible, very clear.
For those interested in git and github:
The following 2 are very good:
and for the most common problems use this link….very very good
https://www.coursera.org/course/datascitoolbox …. has some lessons on git and github
For those who start in R:
– First, install R : http://cran.rstudio.com
– Second and most important, install RStudio: http://www.rstudio.com/products/rstudio/download/
– Third, work always with RStudio. Provides a user interface quite similar to Matlab, and is very easy to use.
– Fourth, do this free online course: https://www.coursera.org/course/rprog
(the more energetics can do the complete series: https://www.coursera.org/specialization/jhudatascience/1?utm_medium=listingPage)
– Fifth, here you have reference information:
but in the web you have tons of information about R.
– The last point, when you look up for information on google about R, use always R written as [R], otherwise you won’t have very useful results…..
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:
This is an incredible series of lectures from
and here is a whole series of lectures on linear algebra and tensor calculus from the same professor that is extraordinarily good
His is also this book: