Machine Learning with SciKit-Learn with Python

The goal of this course is to help the trainee’s expertise working with the python based Scikit–learn library. This training will enable one to implement the concepts of Machine learning using applications by the virtue of Scikit–learn. The sole purpose of this course is to provide a practical understanding of the Scikit–learn library to the trainees. After completing this training, the trainees will be able to endure the application development that requires ML implementation using the Scikit–learn library. In this unit, you will be getting a brief introduction of the concept which includes all the basic details together with the topics that are important to understand. You will understand how this library helps the application by helping the developers in adding machine learning–based concepts. After the mid part of the video, you will be learning about the topics that fall under the court of advanced level concepts. After this unit, you will be able to work to implement the concepts of Machine learning with the help of SciKit–Learn.

Scikit–learn can be defined as the python based library which is used to implement the concepts of machine learning in the application. It could also be explained as the predefined set of functions that is leveraged to bring the features in the application which are considered linked with machine learning. It is the library that consists of various tools for statistical modeling and machine learning. Regression, clustering, and classification are some of the most useful tools that could be found in this library. It is built on top of NumPy, SciPy, and Matplotlib which is one of the reason behind the functions it provides. Being based on python, it will only be supported while implementing things using the python programming language. It can be used the same way as other libraries are used in python but the features it will offer will be unique and focused on Machine learning.