Machine Learning Libraries
We will explore the most commonly used python libraries for Machine Learning.
We'll cover the following...
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()