Optimizing Machine Learning Performance
- Offered byCoursera
Optimizing Machine Learning Performance at Coursera Overview
Duration | 12 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Optimizing Machine Learning Performance at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 4 in the Machine Learning: Algorithms in the Real World Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Approx. 12 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Optimizing Machine Learning Performance at Coursera Course details
- 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.
- To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).
- This is the final course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute (Amii).
Optimizing Machine Learning Performance at Coursera Curriculum
Machine Learning Strategy
Introduction to the course
ML Readiness
Risk Mitigation
Experimental Mindset
Build/Buy/Partner
Setting up a Team
Understanding and Communicating Change
Weekly Summary
IP questions
ML Readiness Review
Risk Mitigation Review
Experimental Mindset Review
Build/Buy/Partner Review
Setting up a Team Review
Communicating Change Review
Responsible Machine Learning
AI 4 Good & for all
Positive Feedback Loops & Negative Feedback Loops
Metric Design & Observing Behaviours
Secondary Effects of Optimization
Regulatory Concerns
Weekly Summary
AI4Good Review
Feedback Loops Review
Metric Design Review
Secondary effects Review
Regulatory Concerns Review
Responsible Machine Learning Review
Machine Learning in Production & Planning
Integrating Info Systems
Users Break Things
Time & Space complexity in production
When do I retrain the model?
Logging ML Model Versioning
Knowledge Transfer
Reporting Performance to Stakeholders
Weekly Summary
Integrating Info Systems Review
Complexity in Production Review
Retrain the Model Review
ML Versioning Review
Knowledge Transfer Review
Reporting to Stakeholders Review
Machine Learning in Production and Planning Review
Care and Feeding of your Machine Learning System
MLPL Recap
Post Deployment Challenges
QuAM Monitoring and Logging
QuAM Testing
QuAM Maintenance
QuAM Updating
Separating Datastack from Production
Dashboard Essentials & Metrics Monitoring
Weekly Summary
Post Deployment Challenges Review
Monitoring & Logging Review
Testing Review
Maintenance Review
Updating Review
Separating Datastack from Production Review
Dashboard Monitoring Review
Optimizing Machine Learning Performance at Coursera Admission Process
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