Machine Learning Libraries

Introduction to machine learning libraries

Data scientists rarely work alone. This means it’s vital to maintain consistent code that can be read and reused by other programmers. Like using WordPress plugins with websites, code libraries make it easy for data scientists to perform common tasks using pre-written code modules.

With WordPress, for example, you can install a comments management plugin called Discuz on a portfolio of websites. Using the same plugin for each website eliminates the need for developers to familiarize themselves with each site’s underlying code. They do, however, need to familiarize themselves with the basic interface and customization settings of the Discuz plugin.

The same logic and benefits apply to machine learning libraries, as complex algorithms and other functions can be called through the same code interface. Moreover, rather than writing the statistical requirements of a regression algorithm over many lines of code, you can call the algorithm from a library like Scikit-learn using just one line of code.

Example:

my_model = LinearRegression() 

Pandas

Pandas is a library for managing and presenting your data. The name “Pandas” comes from the term “panel data,” which refers to Panda’s ability to create a series of panels, similar to sheets in Excel. Pandas can organize structured data as a data frame, a two-dimensional data structure (tabular dataset) with labeled rows and columns similar to a spreadsheet or SQL table.

You can also use Pandas to import and manipulate an external dataset, including CSVComma Separated Values files, as a data frame without affecting the source file as modifications occur inside your development environment.