Machine Learning 简明教程

Machine Learning - Getting Datasets

机器学习模型的优劣取决于其接受训练的数据。因此，获取高质量且相关的数据集是机器学习流程中的关键步骤。我们来看看机器学习的不同数据源以及如何获取。

Machine learning models are only as good as the data they are trained on. Therefore, obtaining good quality and relevant datasets is a critical step in the machine learning process. Let’s see some different sources of datasets for machine learning and how to obtain them.

Public Datasets

有很多公开的数据集，你可以用于机器学习。一些受欢迎的公开数据集源包括 Kaggle、UCI 机器学习存储库、Google 数据集搜索以及 AWS 公共数据集。这些数据集通常用于研究并且向公众开放。

There are many publicly available datasets that you can use for machine learning. Some of the popular sources of public datasets include Kaggle, UCI Machine Learning Repository, Google Dataset Search, and AWS Public Datasets. These datasets are often used for research and are open to the public.

Data Scraping

数据抓取涉及自动从网站或其他来源提取数据。它是一种有用的方法，可以获取尚未作为预先打包数据集提供的数据。然而，确保以合乎道德和合法的方式抓取数据，而且来源可靠且准确非常重要。

Data scraping involves automatically extracting data from websites or other sources. It can be a useful way to obtain data that is not available as a pre-packaged dataset. However, it is important to ensure that the data is being scraped ethically and legally, and that the source is reliable and accurate.

Data Purchase

在一些情况下，可能需要购买机器学习数据集。许多公司销售针对特定行业或用例定制的预先打包数据集。在购买数据集之前，评估其质量以及与你的机器学习项目之间的相关性非常重要。

In some cases, it may be necessary to purchase a dataset for machine learning. Many companies sell pre-packaged datasets that are tailored to specific industries or use cases. Before purchasing a dataset, it is important to evaluate its quality and relevance to your machine learning project.

Data Collection

数据收集涉及从各种来源手动收集数据。这会很耗时并且需要仔细规划，以确保数据准确且与你的机器学习项目相关。这可能涉及调查、采访或其他形式的数据收集。

Data collection involves manually collecting data from various sources. This can be time-consuming and requires careful planning to ensure that the data is accurate and relevant to your machine learning project. It may involve surveys, interviews, or other forms of data collection.

Strategies for Acquiring High Quality Datasets

一旦你识别出你的数据源，确保数据质量好且与你的机器学习项目相关就非常重要。以下是获取高质量数据集的一些策略 −

Once you have identified the source of your dataset, it is important to ensure that the data is of good quality and relevant to your machine learning project. Below are some Strategies for obtaining good quality datasets −

Identify the Problem You Want to Solve

在获取数据集之前，确定使用机器学习想要解决的问题非常重要。这将帮助你确定需要何种类型的数据以及从哪里获取。

Before obtaining a dataset, it is important to identify the problem you want to solve with machine learning. This will help you determine the type of data you need and where to obtain it.

Determine the Size of the Dataset

数据集的大小取决于你努力解决的问题的复杂性。一般来说，你拥有的数据越多，你的机器学习模型的表现越好。然而，确保数据集不是太大且不包含无关或重复的数据非常重要。

The size of the dataset depends on the complexity of the problem you are trying to solve. Generally, the more data you have, the better your machine learning model will perform. However, it is important to ensure that the dataset is not too large and contains irrelevant or duplicate data.

Ensure the Data is Relevant and Accurate

确保数据和你努力解决的问题相关且准确非常重要。确保数据来自可靠的来源并且已被验证。

It is important to ensure that the data is relevant and accurate to the problem you are trying to solve. Ensure that the data is from a reliable source and that it has been verified.

Preprocess the Data

数据预处理涉及清理、标准化和转换数据，以便为机器学习做准备。这一步对于确保机器学习模型可以理解并有效使用数据至关重要。

Preprocessing the data involves cleaning, normalizing, and transforming the data to prepare it for machine learning. This step is critical to ensure that the machine learning model can understand and use the data effectively.