課程信息

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完成時間大約為13 小時
英語(English)
字幕:英語(English)
可分享的證書
完成後獲得證書
100% 在線
立即開始,按照自己的計劃學習。
可靈活調整截止日期
根據您的日程表重置截止日期。
完成時間大約為13 小時
英語(English)
字幕:英語(English)

提供方

Alberta Machine Intelligence Institute 徽標

Alberta Machine Intelligence Institute

教學大綱 - 您將從這門課程中學到什麼

1

1

完成時間為 4 小時

Machine Learning Strategy

完成時間為 4 小時
8 個視頻 (總計 42 分鐘), 1 個閱讀材料, 7 個測驗
8 個視頻
ML Readiness6分鐘
Risk Mitigation5分鐘
Experimental Mindset5分鐘
Build/Buy/Partner7分鐘
Setting up a Team5分鐘
Understanding and Communicating Change7分鐘
Weekly Summary2分鐘
1 個閱讀材料
IP questions10分鐘
6 個練習
ML Readiness Review10分鐘
Risk Mitigation Review10分鐘
Experimental Mindset Review10分鐘
Build/Buy/Partner Review30分鐘
Setting up a Team Review5分鐘
Communicating Change Review5分鐘
2

2

完成時間為 2 小時

Responsible Machine Learning

完成時間為 2 小時
6 個視頻 (總計 27 分鐘)
6 個視頻
Positive Feedback Loops & Negative Feedback Loops6分鐘
Metric Design & Observing Behaviours6分鐘
Secondary Effects of Optimization4分鐘
Regulatory Concerns3分鐘
Weekly Summary2分鐘
6 個練習
AI4Good Review5分鐘
Feedback Loops Review5分鐘
Metric Design Review5分鐘
Secondary effects Review5分鐘
Regulatory Concerns Review5分鐘
Responsible Machine Learning Review30分鐘
3

3

完成時間為 2 小時

Machine Learning in Production & Planning

完成時間為 2 小時
8 個視頻 (總計 33 分鐘)
8 個視頻
Users Break Things3分鐘
Time & Space complexity in production5分鐘
When do I retrain the model?4分鐘
Logging ML Model Versioning4分鐘
Knowledge Transfer4分鐘
Reporting Performance to Stakeholders4分鐘
Weekly Summary2分鐘
7 個練習
Integrating Info Systems Review5分鐘
Complexity in Production Review5分鐘
Retrain the Model Review5分鐘
ML Versioning Review5分鐘
Knowledge Transfer Review5分鐘
Reporting to Stakeholders Review5分鐘
Machine Learning in Production and Planning Review30分鐘
4

4

完成時間為 5 小時

Care and Feeding of your Machine Learning System

完成時間為 5 小時
9 個視頻 (總計 45 分鐘)
9 個視頻
MLPL Recap9分鐘
Post Deployment Challenges6分鐘
QuAM Monitoring and Logging5分鐘
QuAM Testing5分鐘
QuAM Maintenance3分鐘
QuAM Updating5分鐘
Separating Datastack from Production3分鐘
Dashboard Essentials & Metrics Monitoring5分鐘
Weekly Summary1分鐘
7 個練習
Post Deployment Challenges Review5分鐘
Monitoring & Logging Review5分鐘
Testing Review5分鐘
Maintenance Review5分鐘
Updating Review5分鐘
Separating Datastack from Production Review5分鐘
Dashboard Monitoring Review5分鐘

審閱

來自OPTIMIZING MACHINE LEARNING PERFORMANCE的熱門評論

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關於 Machine Learning: Algorithms in the Real World 專項課程

This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning....
Machine Learning: Algorithms in the Real World

常見問題

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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