学习和数据挖掘 英语是什么意思 - 英语翻译

在 中文 中使用 学习和数据挖掘 的示例及其翻译为 英语

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学习和数据挖掘.
基本机器学习和数据挖掘概念,例如分类和群集;.
Basic machine learning and data mining concepts such as classification and clustering;
机器学习和数据挖掘:.
Machine learning and data mining:.
机器学习和数据挖掘通常采用相同的方法,重叠程度很大。
Machine learning and data mining often employ the same methods and overlap significantly.
自然,机器学习和数据挖掘社区开始走到一起。
Naturally, the machine learning and data mining com- munities started to come together.
WEKA机器学习套件:专门用于机器学习和数据挖掘.
WEKA machine learning suite, which is dedicated to machine learning and data mining.
机器学习和数据挖掘之间的一个关键区别是它们如何在我们的日常生活中应用。
One key difference between machine learning and data mining is how they are used and applied in our everyday lives.
ApacheMahout:Mahout提供了一个用于机器学习和数据挖掘的预制算法库,也可用作创建更多算法的环境。
Apache Mahout:Mahout provides a library of pre-made algorithms for machine learning and data mining and also an environment to create more algorithms.
机器学习和数据挖掘:数据科学确实完全包含了这两个领域。
Machine learning and data mining: data science indeed fully encompasses these two domains.
AstroML是一个用于机器学习和数据挖掘的Python模块,它基于numpy,scipy,scikit-learn和matplotlib构建。
AstroML AstroML is a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib, and astropy.
机器学习和数据挖掘的方法是适用的,在各种各样的领域,从处理行业数据科学的需要。
The methods of machine learning and data mining are applicableand needed in a wide variety of fields ranging from process industry to data science.
在2006年,我们启动了Netflix大奖赛,是一个机器学习和数据挖掘的比赛,旨在解决电影评分预测问题。
In 2006 Netflix announced the Netflix Prize, a machine learning and data mining competition for movie rating prediction.
它紧密相连,甚至被认为是机器学习和数据挖掘的代名词。
It is closely linked andeven considered synonymous with machine learning and data mining.
Orange是一个开源数据可视化、机器学习和数据挖掘工具包。
Orange is an open-source data visualization, machine learning and data mining toolkit.
分类是一种重要的机器学习和数据挖掘技术。
Classification is an important machine learning and data mining technology.
特征选择在很多机器学习和数据挖掘场景中都是非常有用的。
Feature ranking canbe incredibly useful in a number of machine learning and data mining scenarios.
机器学习和数据挖掘之间的一个关键区别是它们如何在我们的日常生活中应用。
The main difference between data mining and machine learning is the method to use it and apply to our daily lives.
你将能够构建概率分布,对你的数据应用各种统计测试,并使用标准的机器学习和数据挖掘技术。
You will be able to build probability distributions, apply a variety of statistical tests to your data,and use standard machine learning and data mining techniques.
随着机器学习和数据科学的出现,系统还可能包括决策支持、机器学习研究和数据挖掘项目。
With the advent of machine learning and data science, systems may also include decision support, machine-learning research and data mining projects.
以下为7个步骤用于学习数据挖掘和数据科学。
Here are seven steps for learning data mining and data science.
分析环境,例如Mahout,可为机器学习、数据挖掘和预测分析提供分析模型。
Analytics environments such as Mahout,which supplies analytical models for machine learning, data mining and predictive analytics.
数据挖掘和机器学习都有助于提高收集数据的准确性。
Both data mining and machine learning will help enhance the accuracy of information gathered.
Orange是一个基于组件的可视化编程软件包,用于数据可视化、机器学习、数据挖掘和数据分析。
Orange is a component-based visual programming software package for data visualization,machine learning, data mining, and data analysis.
他研究生毕业于印度理工学院,在机器学习和大规模数据挖掘方面拥有13年工作经验。
He is a post graduate from Indian Institute of Science andhas 13 years of experience in Machine learning and massive scale data mining.
机器学习、深度学习和/或教育数据挖掘在自适应教育中的应用.
Applications of machine learning, deep learning, and/or educational data mining to adaptive education.
机器学习、数据挖掘和人工智能使得对精神疾病的研究和理解发生革命性转变。
Machine learning, data mining, and artificial intelligence are revolutionizing the studyand understanding of mental illness.
DARPA期望在这些计划下开发的系统能够利用机器学习数据挖掘等技术。
DARPA expects the systems developed under theseprograms to make use of techniques like machine learning and data mining.
GraphLabPowerGraph-包含C++实现的GraphLabAPI以及一组基于GraphLabAPI构建的高性能机器学习数据挖掘工具包。
GraphLab PowerGraph- a collection of high-performance machine learning and data mining toolkits built on top of the GraphLab API.
学生将学习数据挖掘的基本工具以及利用机器学习技术解决网络安全问题。
Students will learn the basic tools of data mining and the utilization of machine learning techniques for solving cybersecurity problems.
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