Course Summary
This course teaches how to optimize machine learning model performance. It covers techniques like hyperparameter tuning, model selection, and feature engineering.Key Learning Points
- Learn how to fine-tune your machine learning models for better performance.
- Understand the importance of feature engineering and how it can improve your model's accuracy.
- Explore techniques like hyperparameter tuning and model selection to improve your model's performance.
Related Topics for further study
- Model Optimization
- Hyperparameter Tuning
- Feature Engineering
- Model Selection
- Machine Learning Performance
Learning Outcomes
- Optimize machine learning models for better performance
- Understand the importance of feature engineering in improving model accuracy
- Apply techniques like hyperparameter tuning and model selection to improve model performance
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python and machine learning concepts
- Familiarity with common machine learning libraries like sklearn and TensorFlow
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Hands-on coding exercises
Similar Courses
- Applied Data Science with Python Specialization
- Advanced Machine Learning Specialization
Related Education Paths
Notable People in This Field
- Andrew Ng
- Fei-Fei Li
Related Books
Description
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.
Outline
- 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
Summary of User Reviews
Discover how to optimize machine learning model performance in this comprehensive course on Coursera. Students rave about the course content and practical applications, with many noting that it is a great introduction to machine learning optimization.Key Aspect Users Liked About This Course
Practical applications of the course contentPros from User Reviews
- Clear and concise explanations
- Great introduction to machine learning optimization
- Real-world examples and case studies
- Hands-on exercises and projects
- Excellent instructor support
Cons from User Reviews
- Some technical concepts may be challenging for beginners
- Course structure may be too fast-paced for some
- Not enough focus on deep learning techniques
- Limited interaction with other students
- No certification or credential upon completion
Anna Koop
4.6 Raiting
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