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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/follow-machine-learning-workflow
课程评论：没有评论
课程名称：遵循机器学习工作流程 课程概述：机器学习不仅仅是一项独立的任务或小组任务，而是一个全面的过程。实践者必须从头到尾遵循这个过程，也称为工作流程，才能从机器学习技术中获得最有用的结果。无论最终产品或服务的形式如何，利用工作流程是企业人工智能解决方案成功的关键。本课程是认证人工智能从业者（CAIP）职业证书中的第二门课程，详细探讨机器学习工作流程的每一个步骤，从问题制定开始，到模型展示和部署结束。在前一门课程中，整体工作流程已经介绍，本课程将更深入地研究构成工作流程的重要任务，包括数据分析和模型训练这两项最具实践性的任务。你还将学习如何自动化机器学习任务，确保工作流程可以在需要时重复进行，就像大多数重要的商业流程一样。 最终，本课程为你提供了一个实用框架，你将在接下来的课程中基于此框架构建更多的机器学习模型。 课程大纲： 1. 收集数据集：在本模块中，你将开始收集将用于机器学习项目的输入数据。 2. 分析数据集：你已制定机器学习问题，并确定了潜在的数据集，现在你将分析数据集，以发掘如何最佳利用其信息，准备创建初始机器学习模型。 3. 准备数据集：在将数据集应用于机器学习模型之前，需要执行各种任务以确保数据处于最佳状态。你将使用各种方法来准备数据。 4. 设置和训练模型：在Python环境中设置机器学习模型时，你需要确定能够产生预期结果的算法，并利用这些算法根据训练数据创建模型。初步设置后，可能需要多次测试和调整才能满足要求。 5. 完善模型：完成机器学习模型的训练和调整后，你可以将注意力转向部署模型。这可能包括基于你的发现生成报告，或更复杂的工作，特别是如果它将被纳入可重复的流程或成为软件解决方案的一部分。无论如何，最终化是机器学习工作流程的关键结论。 6. 应用所学：你将参与一个项目，运用本课程中学到的知识解决实际场景。
Part: 1
Title:Collect the Dataset
Description:The previous course in this specialization provided an overview of the machine learning workflow. Now, in this course, you'll dive deeper and actually go through the process step by step. In this first module, you'll begin by collecting the data that will be used as input to your machine learning projects.
Part: 2
Title:Analyze the Dataset
Description:You've formulated a machine learning problem, and have identified a potential dataset to use. Now you'll analyze the dataset to develop ideas on how to make the best use of the information it contains as you prepare to create your initial machine learning model.
Part: 3
Title:Prepare the Dataset
Description:Before a dataset can be used with a machine learning model, there are typically various tasks you need to perform to ensure that data is an optimal state. In this module, you'll use various methods to prepare the data.
Part: 4
Title:Set Up and Train a Model
Description:To set up a machine learning model in an environment like Python, you must determine the algorithm that will produce the results you're after, and then use it to create a model based on your training data. After the initial setup, it may take multiple tests and refinements to produce a model that meets your requirements.
Part: 5
Title:Finalize the Model
Description:Now that you've finished training and tuning a machine learning model, you can turn your attention to deploying it. This may amount to producing a report based on your findings, or it may be much more involved, particularly if it will be incorporated into repeatable processes or become part of a software solution. In either case, finalization is the crucial conclusion to the machine learning workflow.
Part: 6
Title:Apply What You've Learned
Description:You'll work on a project in which you'll apply your knowledge of the material in this course to a practical scenario.
Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution. This second course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate explores each step along the machine learning workflow, from problem formulation all the way to model presentation and deployment. The overall workflow was introduced in the previous course, but now you'll take a deeper dive into each of the important tasks that make up the workflow, including two of the most hands-on tasks: data analysis and model training. You'll also learn about how machine learning tasks can be automated, ensuring that the workflow can recur as needed, like most important business processes. Ultimately, this course provides a practical framework upon which you'll build many more machine learning models in the remaining courses.
