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Practical Machine Learning without Coding

本课程是 No-Code Machine Learning and Data Science for Everyone 专项课程的一部分

位教师：LearnQuest Network

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

3 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

3 小时完成

灵活的计划

自行安排学习进度

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

8 任务¹

AI 评分请参见免责声明

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 No-Code Machine Learning and Data Science for Everyone 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有3个模块

Learn to train and optimize common machine learning models, manage resource limitations, and streamline model building with no-code automation. Analyze output confidently with guided interpretation and error analytics.

单元详情

Unlock practical machine learning for real-world impact—without ever touching code. In this module, gain hands-on experience building standard ML models on modern no-code platforms. Experiment efficiently by launching batch training jobs, automating performance reviews, and resolving errors confidently using intuitive guides. Simplify the art of hyperparameter tuning and learn to optimize model complexity for key business scenarios. Each lesson delivers actionable skills for fast career growth in today’s data-driven industries.

涵盖的内容

7个视频2篇阅读材料3个作业

7个视频总计12分钟

Standard ML Models in No-Code Platforms2分钟
Launch batch model training jobs for quicker experimentation2分钟
Review automated performance metrics for rapid feedback2分钟
Diagnose errors using built-in troubleshooting guides2分钟
Conduct auto-tuning for key model parameters2分钟
Balance model complexity and accuracy with scenario-based optimization2分钟
Record tuning histories for compliance and reproducibility audits2分钟

2篇阅读材料总计10分钟

Action Story: Fixing Model Errors Before the Client Demo5分钟
Action Story: Tuning Under Pressure Before the Launch5分钟

3个作业总计62分钟

Graded Assignment26分钟
Training and Evaluating Models with No-Code Systems30分钟
Coursera generated quiz6分钟

Take your machine learning skills to the next level with no-code tools built for scale. In this module, harness cloud-enabled features to process big data and deploy advanced models—without complexity. Build resilience into your workflows with automated backups, Geo specific model tuning, and integrated auditing to spot bias and keep models performing at their peak. Learn the frameworks that future-proof your work and ensure compliance as you grow.

涵盖的内容

7个视频1篇阅读材料3个作业

7个视频总计12分钟

Advanced Algorithms and Automation for Scale1分钟
Connect no-code tools to cloud resources for scalable computation2分钟
Manage big data workflows using partitioning and streaming features2分钟
Configure auto-backups to safeguard project continuity2分钟
Detect bias and fairness concerns with integrated audit features2分钟
Set automation for periodic retraining triggered by data changes1分钟
Monitor model health with live diagnostic dashboards2分钟

1篇阅读材料总计5分钟

Action Story: Scaling Up Before the Product Launch5分钟

3个作业总计42分钟

Coursera generated quiz26分钟
Building Scalable Models on Cloud No-Code Platforms10分钟
Auditability and Automation in ML Workflows6分钟

Bring machine learning models to life in real business environments—without a single line of code. This module shows how to deploy solutions quickly and reliably using no-code workflow editors and seamless API integrations. Learn to drive measurable impact, automate deployment, and foster ongoing improvement with strong communication and stakeholder engagement. Master the skills that transform ML prototypes into business-critical assets, clear resistance, and set the stage for success.