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所在平台: Udemy |
课程主页: https://www.udemy.com/course/automl-automated-machine-learning-bootcamp-no-code-ml/
课程评论：没有评论
课程名称：AutoML 自动化机器学习训练营（无代码机器学习） 课程概述： “无代码”机器学习（ML）致力于提供一种不需要编写代码的方式，使用户能够构建和部署机器学习模型。这种方法旨在让更多的用户群体能够接触和使用机器学习，包括那些编程基础较弱的人。亚马逊SageMaker是由亚马逊网络服务（AWS）提供的全面管理的机器学习服务，旨在帮助开发人员和数据科学家构建、训练和大规模部署机器学习模型。SageMaker具备内置算法、常见机器学习任务的预构建库，以及多种数据预处理、模型调优和部署工具。它还能与其他AWS服务集成，提供完整的机器学习环境。 在SageMaker中，AutoML指的是机器学习模型的自动选择和调优，以提升模型的准确性和性能。用户可以使用SageMaker的内置算法和库，或采用自定义算法及库。此外，SageMaker还包括自动模型调优功能，可以调节模型的超参数以改善其性能。 SageMaker Studio Canvas是一个允许用户与数据交互的功能，用户可在同一网页界面内构建和可视化工作流程，创建、运行和调试Jupyter笔记本。Canvas为用户提供了一种视觉化和互动的方式来探索、处理和可视化数据，同时用户可以创建Jupyter笔记本并拖放预构建的代码片段（称为“配方”）以快速执行常见的数据预处理、数据可视化和数据分析任务。SageMaker Studio Canvas还允许用户轻松分享他们的笔记本、配方和数据与其他用户，并协作完成项目。这有助于简化机器学习开发过程，加速机器学习模型的开发，并改善团队间的协作。 在本课程中，您将学习： 1. 机器学习项目的生命周期 2. 机器学习基础知识 3. 机器学习的云计算 4. AWS SageMaker Canvas（无代码机器学习）
"No code" machine learning (ML) refers to the use of ML platforms, tools, or libraries that allow users to build and deploy ML models without writing any code. This approach is intended to make ML more accessible to a wider range of users, including those who may not have a strong programming background.Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy machine learning models at scale. SageMaker also includes built-in algorithms, pre-built libraries for common machine learning tasks, and a variety of tools for data pre-processing, model tuning, and model deployment. SageMaker also integrates with other AWS services to provide a complete machine learning environment.AutoML in SageMaker refers to the automatic selection and tuning of machine learning models to improve the accuracy and performance of the models. This can be done by using SageMaker's built-in algorithms and libraries or by using custom algorithms and libraries. SageMaker also includes a feature called Automatic Model Tuning which allows for tuning of the hyper-parameters of the models to improve their performance.SageMaker Studio Canvas is a feature that allows users to interact with their data, build and visualize workflows, and create, run, and debug Jupyter notebooks, all within the same web-based interface. The Canvas provides a visual and interactive way to explore, manipulate and visualize data, and allows users to create Jupyter notebooks and drag-and-drop pre-built code snippets, called "recipes" to quickly perform common data pre-processing, data visualization, and data analysis tasks.SageMaker Studio Canvas also allows users to easily share their notebooks, recipes, and data with other users and collaborate on projects. This helps to simplify the machine learning development process, accelerate the development of machine learning models, and improve collaboration among teams.IN THIS COURSE YOU WILL LEARN:LifeCycle of a Machine Learning ProjectMachine Learning FundamentalsCloud Computing for Machine LearningAWS SageMaker Canvas (NO CODE ML)
