Machine Learning with SciKit-Learn with Python

Get a practical understanding of the Scikit-Learn library and learn the ML implementation

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.

What you’ll learn

  • This Scikit-learn Training has been designed in a manner so that it can contain all the topics that the trainees have to expertise so that they can work effectively with this library. At the starting of the course, you will get to learn about Machine Learning with SciKit-Learn which is one of the important components of this course where you will be learning every single thing about SciKit-Learn..
  • You will be getting deep exposure to python in this training. Once you are done with this course, you will be possessing an ample skillset to work efficiently with the SciKit-Learn library..

Course Content

  • Introduction –> 2 lectures • 13min.
  • NumPy –> 2 lectures • 15min.
  • NumPy Array –> 10 lectures • 1hr 34min.
  • Indexing Arrays of Arrays –> 3 lectures • 24min.
  • Matlplotlib –> 3 lectures • 27min.
  • Pandas –> 8 lectures • 1hr 19min.
  • Scikit Learn –> 4 lectures • 32min.
  • Learning and Predicting –> 5 lectures • 49min.
  • Cross Validation –> 15 lectures • 2hr 37min.
  • Movie Review Analysis –> 2 lectures • 12min.

Machine Learning with SciKit-Learn with Python

Requirements

  • Several topics or concepts are there for which you should have a basic understanding of to make the learning of this library easy for you. The very first thing is the basics of python. As this library is entirely based on python, the trainees need to have a basic understanding of the concepts of python. If you would have worked with python, you will find the concepts covered here pretty simple..
  • The next important concept is the basics of Machine learning. With the help of this library, we will be implementing the concepts of ML. So it is very necessary to understand how it could be used. In this Scikit-learn Training, we have included all the topics that we are considering as the prerequisite here so that the trainees can brush up their understanding before beginning this training..

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.