What you will learn in Structuring Machine Learning Projects Course
- Diagnose errors in machine learning systems and prioritize strategies to address them.
- Understand complex ML scenarios, including mismatched training/test sets and surpassing human-level performance.
- Apply end-to-end learning, transfer learning, and multi-task learning techniques.
- Implement strategic guidelines for goal-setting and apply human-level performance metrics to define key priorities.
Program Overview
ML Strategy
⏱️2 hours
Learn the importance of ML strategy and how to streamline and optimize your ML production workflow.
Topics include orthogonalization, single number evaluation metrics, and understanding human-level performance.
ML Strategy
⏱️3 hours
Develop time-saving error analysis procedures and gain intuition for data splitting.
Explore transfer learning, multi-task learning, and end-to-end deep learning.
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Job Outlook
- Proficiency in structuring ML projects is essential for roles such as Machine Learning Engineer, Data Scientist, and AI Product Manager.
- Skills acquired in this course are applicable across various industries, including technology, healthcare, finance, and more.
- Completing this course can enhance your qualifications for positions that require expertise in machine learning project management.
Specification: Structuring Machine Learning Projects
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