Machine Learning 简明教程

Machine Learning - Implementation

实施机器学习包括几个步骤,其中包括:

Implementing machine learning involves several steps, which include −

Data Collection and Preparation

实施机器学习的第一步是收集将用于训练和测试模型的数据。这些数据应该与机器学习模型的构建所要解决的问题相关。一旦收集了数据,就需要对其进行预处理和清理,以删除任何不一致或缺失的值。

The first step in implementing machine learning is collecting the data that will be used to train and test the model. The data should be relevant to the problem that the machine learning model is being built to solve. Once the data has been collected, it needs to be preprocessed and cleaned to remove any inconsistencies or missing values.

Data Exploration and Visualization

下一步是探索和可视化数据,以深入了解其结构并识别任何模式或趋势。诸如 matplotlib 和 seaborn 等数据可视化工具可用于创建诸如直方图、散点图和热图之类的可视化。

The next step is to explore and visualize the data to gain insights into its structure and identify any patterns or trends. Data visualization tools such as matplotlib and seaborn can be used to create visualizations such as histograms, scatter plots, and heat maps.

Feature Selection and Engineering

选出或构建与问题相关的、能够提高模型精度的现有数据特征。特征工程涉及从现有数据中创建新特征。

The features of the data that are relevant to the problem need to be selected or engineered. Feature engineering involves creating new features from existing data that can improve the accuracy of the model.

Model Selection and Training

在数据经过准备并选择了或构建了特征之后,下一步是选择合适的机器学习算法来训练模型。这涉及将数据分割成训练集和测试集,并使用训练集拟合模型。可以利用各种机器学习算法(如线性回归、逻辑回归、决策树、随机森林、支持向量机和神经网络)来训练模型。

Once the data has been prepared and features selected or engineered, the next step is to select a suitable machine learning algorithm to train the model. This involves splitting the data into training and testing sets and using the training set to fit the model. Various machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks can be used to train the model.

Model Evaluation

在训练模型后,需要对其进行评估,以确定其性能。可以利用精确度、准确度、召回率和 F1 分数等度量来评估模型的性能。也可以使用交叉验证技术来测试模型的性能。

After training the model, it needs to be evaluated to determine its performance. The performance of the model can be evaluated using metrics such as accuracy, precision, recall, and F1 score. Cross-validation techniques can also be used to test the model’s performance.

Model Tuning

可以通过调节其超参数来提高模型的性能。超参数不是从数据中学习的设置,而是由用户设置的。可以使用网格搜索和随机搜索等技术找到这些超参数的最优值。

The performance of the model can be improved by tuning its hyperparameters. Hyperparameters are settings that are not learned from the data, but rather set by the user. The optimal values for these hyperparameters can be found using techniques such as grid search and random search.

Deployment and Monitoring

在训练和调整模型后,需要将其部署到生产环境中。部署过程涉及将模型集成到业务流程或系统中。还需要定期监控模型以确保其继续良好运行,并找出需要解决的任何问题。

Once the model has been trained and tuned, it needs to be deployed to a production environment. The deployment process involves integrating the model into the business process or system. The model also needs to be monitored regularly to ensure that it continues to perform well and to identify any issues that need to be addressed.

上述每个步骤都需要不同的工具与技术,而成功的实施则需要将技术与业务技能相结合。

Each of the above steps requires different tools and techniques, and successful implementation requires a combination of technical and business skills.

Choosing the Language and IDE for ML Development

要开发机器学习应用程序,您必须决定平台、IDE 和用于开发的语言。有几种选择。其中大多数可以轻松满足您的要求,因为它们都提供了到目前为止讨论的 AI 算法的实现。

To develop ML applications, you will have to decide on the platform, the IDE and the language for development. There are several choices available. Most of these would meet your requirements easily as all of them provide the implementation of AI algorithms discussed so far.

如果您自己开发机器学习算法,则需要仔细理解以下几个方面:

If you are developing the ML algorithm on your own, the following aspects need to be understood carefully −

The language of your choice - 这本质上是您精通机器学习开发中支持的一种语言。

The language of your choice − this essentially is your proficiency in one of the languages supported in ML development.

The IDE that you use - 这将取决于您对现有 IDE 的熟悉程度和舒适程度。

The IDE that you use − This would depend on your familiarity with the existing IDEs and your comfort level.

Development platform - 有几个平台可用于开发和部署。其中大部分都是免费使用的。在某些情况下,您可能需要在使用量超过一定数量后支付许可费。以下是备选语言、IDE 和平台的简要列表,供您参考。

Development platform − There are several platforms available for development and deployment. Most of these are free-to-use. In some cases, you may have to incur a license fee beyond a certain amount of usage. Here is a brief list of choice of languages, IDEs and platforms for your ready reference.

Language Choice

以下是支持机器学习开发的语言列表:

Here is a list of languages that support ML development −

  1. Python

  2. R

  3. Matlab

  4. Octave

  5. Julia

  6. C++

  7. C

此列表并不是全面详尽的;但是,它涵盖了许多用于机器学习开发的流行语言。根据您的舒适程度,选择一种语言进行开发、开发模型并进行测试。

This list is not essentially comprehensive; however, it covers many popular languages used in machine learning development. Depending upon your comfort level, select a language for the development, develop your models and test.

IDEs

以下是支持机器学习开发的 IDE 列表:

Here is a list of IDEs which support ML development −

  1. R Studio

  2. Pycharm

  3. iPython/Jupyter Notebook

  4. Julia

  5. Spyder

  6. Anaconda

  7. Rodeo

  8. Google –Colab

上述列表并非全面详尽。每个都有其优点和缺点。鼓励读者在缩小范围到一个之前尝试这些不同的 IDE。

The above list is not essentially comprehensive. Each one has its own merits and demerits. The reader is encouraged to try out these different IDEs before narrowing down to a single one.