Machine learning Libraries
Method 1
The way to install the packages in python is to open a command line in administrator mode, type "pip install package_name" and enter.
Where the package name is the one we are going to install. It should be remembered that python is case sensitive. The package name must be known exactly.
When installing a package this way it can already be used in our code.
When installing a package this way it can already be used in our code.
For example:
We want to install the Numpy library which is a library optimized for mathematics, so we proceed from this mod
We want to install the Numpy library which is a library optimized for mathematics, so we proceed from this mod
We run and this processes downloads and installs the most recent stable version of numpy
Method 2
Sometimes it is necessary to download the package and then install it through the command line, this process takes a little longer but is as effective as method 1.
First is to download the package with the format ".whl"; This page provides several libraries that we can use in windows. Second, we open a command line and type "pip install <packagepath>", where the package path is the absolute path of the package. We finally run and wait to make use of it.
For example:
We will install Numpy, in this case it is necessary to download the version with the appropriate characteristics. So I select the version for Python 3.5 for windows 32 (in my case)
The file is shown as cp35 for Python 3.5, cp27 for python 2.7 and thus:
For example:
We will install Numpy, in this case it is necessary to download the version with the appropriate characteristics. So I select the version for Python 3.5 for windows 32 (in my case)
The file is shown as cp35 for Python 3.5, cp27 for python 2.7 and thus:
Download the file.
In my case it is in my download folder, so I start a command line in administrator mode and type:
Libraries to download
Either by method 1 or method 2 the libraries to perform the codes for analysis are:- Numpy (Mathematics): http://www.numpy.org/
- Sci-kit learn (machine learning ): http://scikit-learn.org/stable/index.html
- Scipy (python for science): https://www.scipy.org/
- Matplolib (graphs): http://matplotlib.org/
- Theano (optimized library for numerical analysis): http://www.deeplearning.net/software/theano/
- Tensorflow (deep learnign artificial neural network): https://www.tensorflow.org/
- Keras (Deep learning Artificial Neural Networks, using Tensorflow or Theano for your processes): https://keras.io/
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