Structuring Machine Learning Projects

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What Will You Learn?

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate

About This Course

Provider: coursera
Format: Online
Duration: 6 hours to complete [Approx]
Target Audience: intermediate
Learning Objectives: you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning 
project leader.
Course Prerequisites: This course is part of the Deep Learning SpecializationWhen you enroll in this course, you'll also be enrolled in this 
Specialization.
Assessment and Certification: Earn a Certificate upon completion from the relevant Provider
Instructor: Andrew Ng
Key Topics: ML,Evaluation Metric,Optimizing Metric,Human-level Performance
Topic Covered: 
  1. - Why ML Strategy
  2. - Preview module
  3. - Orthogonalization
  4. - Single Number Evaluation Metric
  5. - Satisficing and Optimizing Metric
  6. - Train/Dev/Test Distributions
  7. - Size of the Dev and Test Sets
  8. - When to Change Dev/Test Sets and Metrics?
  9. - Why Human-level Performance?
  10. - Avoidable Bias
  11. - Understanding Human-level Performance
  12. - Surpassing Human-level Performance
  13. - Improving your Model Performance•
  14. - Andrej Karpathy Interview



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