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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-with-scikit-learn-with-python-examturf/
课程评论：没有评论
课程名称：使用Python的SciKit-Learn进行机器学习 课程概述：本课程旨在帮助学员掌握基于Python的Scikit-learn库的使用。通过这个培训，学员将能够实现机器学习的概念，并运用Scikit-learn库进行应用开发。课程的核心目标是为学员提供Scikit-learn库的实用理解，完成培训后，学员将能够进行需要机器学习实现的应用开发。 在本单元中，学员将获得机器学习的基本概念介绍，包括重要主题的详细信息。学员将了解该库如何帮助开发人员将机器学习概念融入应用之中。视频的中期将介绍更高级的概念。在完成该单元后，学员将能够利用Scikit-Learn实现机器学习的相关概念。 Scikit-learn是一个基于Python的库，用于在应用程序中实现机器学习概念。它可以被视为一组预定义的函数，旨在为应用程序提供与机器学习相关的功能。该库包含多个用于统计建模和机器学习的工具，例如回归、聚类和分类，这些都是极为有用的功能。Scikit-learn建立在NumPy、SciPy和Matplotlib的基础上，这也是其能够提供多种功能的原因之一。由于该库基于Python，因此在实施时仅支持使用Python编程语言。Scikit-learn的使用方式与其他Python库相似，但它所提供的功能独特且专注于机器学习。
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.
