Python is a versatile general purpose programming language. It is an interpreted language and can run interactively from a console. It does not require a compiler like C++ or Java, so the development time tends to be shorter. It is available for free download and can be installed on many different operating systems including UNIX, Windows, and Macintosh. It is especially popular for scientific and mathematical applications. Python is relatively easy to learn compared to languages such as C++ and Java, with similar tasks using fewer lines of code.

Python is not the only platform for machine learning, but it is certainly one of the most used. One of its major alternatives is R. Like Python, it is open source, and while it is popular for applied machine learning, it lacks the large development community of Python. R is a specialized tool for machine learning and statistical analysis. Python is a general-purpose, widely-used programming language that also has excellent...

The Ipython package has had some significant changes with the release of version 4. A former monolithic package structure, it has been split into sub-packages. Several IPython projects have split into their own separate project. Most of the repositories have been moved to the Jupyter project (jupyter.org).

At the core of IPython is the IPython console: a powerful interactive interpreter that allows you to test your ideas in a very fast and intuitive way. Instead of having to create, save, and run a file every time you want to test a code snippet, you can simply type it into a console. A powerful feature of IPython is that it decouples the traditional read-evaluate-print loop that most computing platforms are based on. IPython puts the evaluate phase into its own process: a kernel (not to be confused with the kernel function used in machine learning algorithms). Importantly, more than one client can access the kernel. This means you can run code in a number of files and access...

The SciPy stack consists of Python along with the most commonly used scientific, mathematical, and ML libraries. (visit: scipy.org). These include NumPy, Matplotlib, the SciPy library itself, and IPython. The packages can be installed individually on top of an existing Python installation, or as a complete distribution (distro). The easiest way to get started is using a distro, if you have not got Python installed on your computer. The major Python distributions are available for most platforms, and they contain everything you need in one package. Installing all the packages and their dependencies separately does take some time, but it may be an option if you already have a configured Python installation on your machine.

Most distributions give you all the tools you need, and many come with powerful developer environments. Two of the best are Anaconda (www.continuum.io/downloads) and Canopy (http://www.enthought.com/products/canopy/). Both have free and commercial...

We should know that there is a hierarchy of types for representing data in Python. At the root are immutable objects such as integers, floats, and Boolean. Built on this, we have sequence types. These are ordered sets of objects indexed by non-negative integers. They are iterative objects that include strings, lists, and tuples. Sequence types have a common set of operations such as returning an element (s[i]) or a slice (s[i:j]), and finding the length (len(s)) or the sum (sum(s)). Finally, we have mapping types. These are collections of objects indexed by another collection of key objects. Mapping objects are unordered and are indexed by numbers, strings, or other objects. The built-in Python mapping type is the dictionary.

NumPy builds on these data objects by providing two further objects: an N-dimensional array object (ndarray) and a universal function object (ufunc). The ufunc object provides element-by-element operations on ndarray objects, allowing typecasting and array broadcasting...