Mathworks R2019a 统计与机器学习工具箱资源下载

Mathworks R2019a 统计与机器学习工具箱资源下载

【下载地址】MathworksR2019a统计与机器学习工具箱资源下载 Mathworks R2019a 统计与机器学习工具箱资源下载本仓库提供Mathworks R2019a版本的统计与机器学习工具箱(Statistics and Machine Learning Toolbox)的用户指南和发布说明的资源文件下载 【下载地址】MathworksR2019a统计与机器学习工具箱资源下载 项目地址: https://gitcode.com/open-source-toolkit/baaf6

本仓库提供Mathworks R2019a版本的统计与机器学习工具箱(Statistics and Machine Learning Toolbox)的用户指南和发布说明的资源文件下载。

资源文件内容

  • 用户指南:详细介绍了如何使用统计与机器学习工具箱进行数据分析、模型构建和预测等操作。
  • 发布说明:包含了该工具箱在R2019a版本中的新功能、改进和已知问题。

适用人群

  • 使用Mathworks MATLAB R2019a版本的用户。
  • 需要进行统计分析和机器学习建模的研究人员和工程师。

使用说明

  1. 下载本仓库中的资源文件。
  2. 解压缩文件后,您可以查看用户指南和发布说明,以了解如何使用统计与机器学习工具箱。

注意事项

  • 请确保您使用的是Mathworks MATLAB R2019a版本,以确保工具箱的兼容性。
  • 本资源文件仅供学习和研究使用,请勿用于商业用途。

希望本资源对您的学习和研究有所帮助!

【下载地址】MathworksR2019a统计与机器学习工具箱资源下载 Mathworks R2019a 统计与机器学习工具箱资源下载本仓库提供Mathworks R2019a版本的统计与机器学习工具箱(Statistics and Machine Learning Toolbox)的用户指南和发布说明的资源文件下载 【下载地址】MathworksR2019a统计与机器学习工具箱资源下载 项目地址: https://gitcode.com/open-source-toolkit/baaf6

创作声明:本文部分内容由AI辅助生成(AIGC),仅供参考

Pratap Dangeti, "Statistics for Machine Learning" English | ISBN: 1788295757 | 2017 | EPUB | 311 pages | 12 MB Key Features Learn about the statistics behind powerful predictive models with p-value, ANOVA, F-statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of machine learning with the help of this example-rich guide in R & Python. Book Description Complex statistics in machine learning worries a lot of developers. Knowing statistics helps in building strong machine learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for machine learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and make you comfortable with it. You will come across programs for performing tasks such as model, parameters fitting, regression, classification, density collection, working with vectors, matrices, and more.By the end of the book, you will understand concepts of required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problems. What you will learn Understanding Statistical & Machine learning fundamentals necessary to build models Understanding major differences & parallels between statistics way of solving problem & machine learning way of solving problem Know how to prepare data and "feed" the models by using the appropriate machine learning algorithms from the adequate R & Python packages Analyze the results and tune the model appropriately to his or her own predictive goals Understand concepts of required statistics for Machine Learning Draw parallels between statistics and machine learning Understand each component of machine learning models and see impact of changing them
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