This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of this course you will have all the tools and understanding you need to confidently roll out a machine learning project and prepare to optimize it in your business context.
提供方
Machine Learning: Algorithms in the Real World 專項課程Alberta Machine Intelligence Institute課程信息
18,975 次近期查看
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。
第 4 門課程（共 4 門）
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）
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。
第 4 門課程（共 4 門）
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
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
教學大綱 - 您將從這門課程中學到什麼
完成時間為 4 小時
Machine Learning Strategy
This week we'll present tools for understanding the overall strategy your business needs in order to see the best returns on ML investment. From understanding the current status to navigating ownership and setting up a team, this week is about understanding applied machine learning in a successful business context.
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
完成時間為 2 小時
Responsible Machine Learning
This week we'll talk about the broader context of machine learning: how as developers we have responsibilities regarding how our technology will be used. Using case studies and existing frameworks we'll give you the tools to figure out your own ethical approach to realize the best outcomes while deploying machine learning in the real world.
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
完成時間為 2 小時
Machine Learning in Production & Planning
An important aspect of machine learning in the real world is considering how your machine learning models are integrated with existing systems, and what effect they have on your operations. This week we'll review things you should consider as you turn QuAMs and machine learning models into operational tools.
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
完成時間為 5 小時
Care and Feeding of your Machine Learning System
Work doesn't end just because your model is deployed! In our final week we'll go over all the things you need to consider in the context of an actual working 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
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.
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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.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
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Is financial aid available?
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