Machine Learning with Python

所在平台: CourseraArchive

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大学或机构: CourseraNew

课程主页: https://www.coursera.org/archive/machine-learning-with-python

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课程简介

IBM

课程大纲

In this week, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You’ll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.

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课程详情

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

使用Python进行机器学习:这门课程将深入探讨使用易学且广为人知的编程语言Python进行机器学习的基础。 在本课程中,我们将回顾两个主要部分: 首先,您将学习机器学习的目的及其在现实世界中的应用。 其次,您将获得机器学习主题的一般概述,例如监督学习与非监督学习,模型评估和机器学习算法。 在本课程中,您将通过现实生活中的机器学习示例进行练习,并了解它如何以您可能未曾想到的方式影响社会! 在接下来的几周中,每周只需要花费几个小时,便可以达到目的。 1)要添加到简历中的新技能,例如回归,分类,聚类,sci-kit学习和SciPy 2)您可以添加到投资组合中的新项目,包括癌症检测,预测经济趋势,预测客户流失,推荐引擎等等。 3)并获得机器学习证书,以证明您的能力,并可以在网上或线下喜欢的任何地方共享它,例如LinkedIn个人资料和社交媒体。 如果您选择参加本课程并获得Coursera课程证书,则在成功完成课程后还将获得IBM数字徽章。

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