Optimizing Machine Learning Performance
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.
Monthly Subscription Starting at AED 99 + VAT
- Level Foundation
- المدة 12 ساعات hours
- الطبع بواسطة Alberta Machine Intelligence Institute
-
Offered by
عن
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).الوحدات
Planning
-
4
Videos
-
3
Assignment
3
Assignment
- ML Readiness Review
- Risk Mitigation Review
- Experimental Mindset Review
4
Videos
- Introduction to the course
- ML Readiness
- Risk Mitigation
- Experimental Mindset
Ownership & Products
-
1
Videos
-
1
Readings
-
1
Discussion
-
1
Assignment
1
Assignment
- Build/Buy/Partner Review
1
Discussions
- Intellectual Property to You
1
Videos
- Build/Buy/Partner
1
Readings
- IP questions
Teamwork & Communication
-
3
Videos
-
1
PeerReview
-
2
Assignment
2
Assignment
- Setting up a Team Review
- Communicating Change Review
1
Peer Review
- Positioning Your Company
3
Videos
- Setting up a Team
- Understanding and Communicating Change
- Weekly Summary
Feedback & Fairness
-
2
Videos
-
1
Discussion
-
2
Assignment
2
Assignment
- AI4Good Review
- Feedback Loops Review
1
Discussions
- Feedback Systems affecting you
2
Videos
- AI 4 Good & for all
- Positive Feedback Loops & Negative Feedback Loops
You are what you Optimize! Design Considerations
-
2
Videos
-
2
Assignment
2
Assignment
- Metric Design Review
- Secondary effects Review
2
Videos
- Metric Design & Observing Behaviours
- Secondary Effects of Optimization
Legalities and Best Practices
-
2
Videos
-
2
Assignment
2
Assignment
- Regulatory Concerns Review
- Responsible Machine Learning Review
2
Videos
- Regulatory Concerns
- Weekly Summary
Design Considerations
-
3
Videos
-
2
Assignment
2
Assignment
- Integrating Info Systems Review
- Complexity in Production Review
3
Videos
- Integrating Info Systems
- Users Break Things
- Time & Space complexity in production
Deployment issues & Operational processes
-
2
Videos
-
2
Assignment
2
Assignment
- Retrain the Model Review
- ML Versioning Review
2
Videos
- When do I retrain the model?
- Logging ML Model Versioning
Communicating Technical Content
-
3
Videos
-
3
Assignment
3
Assignment
- Knowledge Transfer Review
- Reporting to Stakeholders Review
- Machine Learning in Production and Planning Review
3
Videos
- Knowledge Transfer
- Reporting Performance to Stakeholders
- Weekly Summary
Mapping the Model Lifecycle
-
2
Videos
-
1
Assignment
1
Assignment
- Post Deployment Challenges Review
2
Videos
- MLPL Recap
- Post Deployment Challenges
Maintenance Checkpoints
-
4
Videos
-
4
Assignment
4
Assignment
- Monitoring & Logging Review
- Testing Review
- Maintenance Review
- Updating Review
4
Videos
- QuAM Monitoring and Logging
- QuAM Testing
- QuAM Maintenance
- QuAM Updating
Scaling Up
-
3
Videos
-
1
PeerReview
-
2
Assignment
2
Assignment
- Separating Datastack from Production Review
- Dashboard Monitoring Review
1
Peer Review
- Machine Learning Project Plan
3
Videos
- Separating Datastack from Production
- Dashboard Essentials & Metrics Monitoring
- Weekly Summary
Auto Summary
"Optimizing Machine Learning Performance" is a comprehensive course designed for aspiring data scientists and AI enthusiasts who aim to master the intricacies of applied machine learning. Created by Coursera in collaboration with the Alberta Machine Intelligence Institute (Amii), this foundational course guides learners through a complete machine learning project, focusing on preparing a maintenance roadmap and optimizing performance in a business context. Throughout the course, participants will learn to handle changing data, identify and mitigate unintended effects, and develop procedures to operationalize and sustain machine learning models. By the end of this course, learners will be equipped with the necessary tools and insights to confidently implement and optimize machine learning projects. Ideal candidates for this course should possess a beginner-level understanding of Python programming, including familiarity with conditionals, loops, and data structures like lists and dictionaries. A basic grasp of linear algebra and statistics is also recommended. Spanning approximately 720 minutes of immersive learning, the course is available under Coursera's Starter and Professional subscription plans. Whether you're looking to solidify your machine learning skills or seeking to apply AI solutions effectively in a business environment, this course offers valuable knowledge and practical strategies to achieve your goals.
Anna Koop