Python has an established popularity among individuals who perform machine learning because of its easy-to-write and easy-to-understand code structure as well as a wide variety of open source libraries. A few of such open source libraries that we will be using in the coming chapters have been introduced below.
Numerical Python is a library used for scientific computing. It works on an N-dimensional array object and provides basic mathematical functionality such as size, shape, mean, standard deviation, minimum, maximum as well as some more complex functions such as linear algebraic functions and Fourier transform. You will learn more about these as we move ahead in this tutorial.
This library provides highly efficient and easy-to-use data structures such as series, dataframes and panels. It has enhanced Python’s functionality from mere data collection and preparation to data analysis. The two libraries, Pandas and NumPy, make any operation on small to very large dataset very simple. To know more about these functions, follow this tutorial.
Science Python is a library used for scientific and technical computing. It provides functionalities for optimization, signal and image processing, integration, interpolation and linear algebra. This library comes handy while performing machine learning. We will discuss these functionalities as we move ahead in this tutorial.
This library is a SciPy Toolkit widely used for statistical modelling, machine learning and deep learning, as it contains various customizable regression, classification and clustering models. It works well with Numpy, Pandas and other libraries which makes it easier to use.
Like Scikit Learn, this library is used for statistical data exploration and statistical modelling. It also operates well with other Python libraries.
This library is used for data visualization in various formats such as line plot, bar graph, heat maps, scatter plots, histogram etc. It contains all the graph related functionalities required from plotting to labelling. We will discuss these functionalities as we move ahead in this tutorial.
These libraries are very essential to start with machine learning with any sort of data.
Beside the ones discussed above, another library especially significant to deal with time series is −
This library, with its two modules − datetime and calendar, provides all necessary datetime functionality for reading, formatting and manipulating time.
We shall be using these libraries in the coming chapters.