Machine Learning with R, 2nd Edition 免积分下载
2018-03-29 08:53
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Machine Learning with R 2nd.Edition
Machine Learning with R
本书介绍
机器学习的核心是将数据转化为可操作的知识,这使得机器学习非常适合当今的大数据时代。鉴于r€日益突出“一个跨平台、零成本的统计编程环境-€”,从没有比现在更好的时候开始将机器学习应用于您的数据。无论您是数据分析新手还是老手,具有r的机器学习提供了一套强大的工具。方法可以快速、轻松地从数据中获取洞察力。想要将你的数据转化为可操作的知识,预测产生真正影响的结果,并不断地发展洞察力?r让你能够获得掌握卓越机器学习技术所需的尖端力量。更新并升级到最新的图书馆和最新的思维,第二版的机器学习与R一起为您提供了对专业数据科学的这一基本技能的严格介绍。 在不偏离技术理论的情况下,它被写入提供专注和实践的知识,以帮助您构建算法并处理您的数据,而以前的经验很少。用这本书你-€。™我会发现你需要的所有分析工具,从复杂的数据中获得洞察力,并学习如何为你的特定需求选择正确的算法。™我将学习运用机器学习方法来处理常见的任务,包括分类、预测、预测、市场分析和聚类。改变你对数据的看法;用r发现机器学习。你会学到什么
利用R 语言的能力,在实际的数据科学应用中建立通用的机器学习算法。掌握r技术,清理和准备数据进行分析,并可视化结果。发现不同类型的机器学习模型,学习哪种方法最适合您的数据需求和解决您的分析问题。用贝叶斯和最近的方法对数据进行分类。使用贝叶斯和近邻方法预测值。r建立决策树、规则和支持向量机。用线性回归预测数值,用神经网络对数据建模。评估和改进机器学习模型的性能。学习专门的机器学习技术,用于文本挖掘、社交网络数据、大数据等等。目录
Chapter 1: Introducing Machine LearningChapter 2: Managing and Understanding DataChapter 3: Lazy Learning – Classification Using Nearest NeighborsChapter 4: Probabilistic Learning – Classification Using Naive BayesChapter 5: Divide and Conquer – Classification Using Decision Trees and RulesChapter 6: Forecasting Numeric Data – Regression MethodsChapter 7: Black Box Methods – Neural Networks and Support Vector MachinesChapter 8: Finding Patterns – Market Basket Analysis Using Association RulesChapter 9: Finding Groups of Data – Clustering with k-meansChapter 10: Evaluating Model PerformanceChapter 11: Improving Model PerformanceChapter 12: Specialized Machine Learning Topics下载地址:Packt Machine Learning with R 2nd.Edition.pdf
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