Statistics for Data Science |百度网盘|rapidgator|nitroflare

Statistics for Data Science

其他教程 killking 1评论
Statistics for Data Science
Statistics for Data Science
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2.5 Hours | 613 MB
Genre: eLearning | Language: English

Do you wish to be a data scientist but don't know where to begin? Want to implement statistics for data science? Want to get acquainted with R programs? Want to learn about the logic involved in computing statistics? If so, then this is the course for you.

This course will take you through an entire statistics odyssey, from knowing very little to becoming comfortable with using various statistical methods with data science tasks. It starts off with simple statistics and then moves on to statistical methods that are used in data science algorithms. R programs for statistical computation are clearly explained along with the logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.

By the end of the course, you will be comfortable with performing various statistical computations for data science programmatically.

Statistics for Data Science

Download rapidgator

Download nitroflare

Download 百度云


sorry! The following hidden content sponsorship VIP members only.

您必须 登录 才能发表评论!

网友最新评论 (1)

  1. 数据科学的统计学 你是否希望成为一名数据科学家,但却不知道从何下手呢,或希望对数据科学应用统计学,或是希望掌握R,或是希望了解计算科学所包含的逻辑?如果是的话,那么本教程适用于你。 本教程将会带你进行一次统计学之旅,从只知道一点点到熟练掌握各种统计方面应用于数据科学任务。教程从简单的统计学开始,然后逐渐过渡到用在数据科学算法中的统计方法。用于统计计算的R会被具有逻辑性地讲解到。你将学习到各种属性概念,如变量、标准方程、改了、矩阵计算等。 你将只学习在数据科学任务中实施统计学,如数据清晰、挖掘和分析所用到的知识。你将学习用来执行线性回归、正则化、模型评估、boosting、SVM以及处理神经网络所需要的统计学技术。 学习完本教程,你将能够自如地执行各种用于数据科学的统计计算。
    wilde(特殊组-翻译)3个月前 (05-21)