MACHINE LEARNING PROBLEM 中文是什么意思 - 中文翻译

• 机器学习问题
• 一个机器学习问题

在 英语 中使用 Machine learning problem 的示例及其翻译为 中文

A machine learning problem consist of three things:.
机器学习问题由三样东西组成：.

The recommended approach to solving machine learning problems is to:.
解决机器学习问题的推荐做法是：.

A machine learning problem, how do you load that data in Octave?
如果你有一个机器学习问题，你怎样把数据加载到Octave中？?

The recommended approach to solving machine learning problems is to:.
推荐的方法来解决机器学习问题是：.

Not every machine learning problem has to be solved from scratch, however.
然而并非所有机器学习问题都只能从零开始着手解决。

Many data scientists see a cool new machine learning problem.
许多数据科学家都看到了很酷的新机器学习问题。

Not every machine learning problem has to be solved from scratch, however.
然而，并不是所有的机器学习问题都必须从头开始解决。

I have said it before, working machine learning problems is addictive.
我之前说过，处理机器学习的问题是会让人上瘾的。

Every Machine Learning problem starts with data, such as a list of emails, posts, or tweets.
每一个机器学习问题都始于数据，比如一组邮件、帖子或是推文。

Here we really faced a machine learning problem,” said Kouider.
在这里，我们确实面临着一个机器学习的问题，”Kouider说。

For example, Grover's algorithm could be used for some machine learning problems.
例如，Grover的算法可用于解决一些机器学习问题。

Every Machine Learning problem starts with data, such as a list of emails, posts, or tweets.
每个机器学习问题都始于数据，如一系列的电子邮件、帖子或推文。

Text recognition in a natural environment is a challenging computer vision and machine learning problem.
自然环境中的文本识别是一个具有挑战性的计算机视觉和机器学习问题。

Every Machine Learning problem starts with data, such as a list of emails, posts, or tweets.
每一个机器学习问题都是从数据开始的，比如电子邮件、帖子或推文。

Predicting housing prices in Portland is one popular machine learning problem for supervised learning..
预测波特兰的房价是受监督学习的一个普遍的机器学习问题。

In most Supervised Machine Learning problems we need to define a model and estimate its parameters based on a training dataset.
在大多数监督性机器学习问题中，我们需要定义一个模型并基于训练数据集预估其参数。

H2O built Driverless AI with popular use cases built-in, but it can't solve every machine learning problem.
H2O.ai用颇受欢迎的已有使用案例开发了DriverlessAI，但它仍无力解决每一个机器学习问题。

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job.
解决机器学习问题的最困难的部分通常是找到合适的工作估算量。

This is an example of binary- or two-class- classification, an important and widely applicable kind of machine learning problem.
这是一个典型的二分类问题，是一种重要且广泛适用的机器学习问题。

Sometimes the hardest part of solving a machine learning problem can be searching the optimal estimator for the job.
解决机器学习问题的最困难的部分通常是找到合适的工作估算量。

This is an example of binary- or two-class- classification, an important and widely applicable kind of machine learning problem.
这是一个二元（binary）或者二分类问题，一种重要且应用广泛的机器学习问题。

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job.
通常，解决机器学习问题的最困难的部分可能是找到恰当的的评估器(estimator)。

This is an example of binary- or two-class- classification, an important and widely applicable kind of machine learning problem.
这是一个二元分类（又称为两类分类）的示例，也是一种重要且广泛适用的机器学习问题。

Sometimes the hardest part of solving a machine learning problem can be searching the optimal estimator for the job.
通常，解决机器学习问题的最困难的部分可能是找到恰当的的评估器(estimator)。

Text recognition in a natural environment like cities, roads and businesses is a challenging computer vision(CV) and machine learning problem.
在城市、道路和商户等自然环境中做文本识别，这是一个具有挑战性的计算机视觉和机器学习问题。

After we understand the type of machine learning problem we are working with, we can think about the type of data to collect and….
在理解了我们需要解决的机器学习问题之后，我们要思考需要收集什么数据以及我们可以用什么算法。

It covers the history of Apache Spark, how to install it using Python, RDD/Dataframes/Datasets and then rounds-up by solving a machine learning problem.
它先容了ApacheSpark的历史以及如何使用Python、RDD/Dataframes/Datasets安装它，然后通过解决机器学习问题，对自己的常识点进行查漏补缺。

Once we have defined the business problem and decomposed into machine learning problems, we need to dive deeper into the data.
一旦我们定义了业务问题，并且分解成了机器学习问题，我们接下来需要深入了解数据。

However, in many machine learning problems the presence of multimodality, particularly in problems involving spaces of high dimensionality, can be less obvious.
然而，在许多机器学习问题中，尤其是涉及到高维空间的问题中，多峰性质的存在并不显然。

Here, based on our specific machine learning problems, we apply useful algorithms like regressions, decision trees, random forests, etc.
这里，基于具体的机器学习问题，我们要应用有效的算法，如回归，决策树，随机森林等。