Structuring Machine Learning Projects Course Syllabus

Overview: This course provides a practical framework for managing and structuring machine learning projects effectively, focusing on strategic decision-making and error diagnosis. You'll learn how to prioritize efforts, evaluate models efficiently, and navigate complex scenarios such as mismatched training/test distributions. With approximately 10 hours of content, this self-paced course includes hands-on assignments and real-world case studies, ideal for beginners with foundational ML knowledge.

Module 1: ML Strategy

Estimated time: 2 hours

• Importance of ML strategy
• Orthogonalization in ML systems
• Single number evaluation metrics
• Understanding human-level performance

Module 2: ML Strategy (Continued)

Estimated time: 3 hours

• Error analysis procedures
• Data splitting strategies
• Transfer learning applications
• Multi-task learning concepts
• End-to-end deep learning

Module 3: Diagnosing Errors in ML Systems

Estimated time: 2 hours

• Identifying bias and variance
• Prioritizing error reduction strategies
• Using evaluation metrics effectively

Module 4: Managing Complex ML Scenarios

Estimated time: 2 hours

• Handling mismatched training/test sets
• Strategies when surpassing human-level performance
• Adjusting data distribution assumptions

Module 5: Strategic Guidelines for ML Projects

Estimated time: 1 hour

• Setting clear project goals
• Applying human-level performance as a benchmark
• Time-saving best practices

Module 6: Final Project

Estimated time: 2 hours

• Analyze a real-world ML project scenario
• Diagnose errors and propose improvements
• Submit a structured project report with strategic recommendations

Prerequisites

• Familiarity with basic machine learning concepts
• Understanding of supervised learning algorithms
• Basic experience with Python and ML frameworks

What You'll Be Able to Do After

• Diagnose errors in machine learning systems
• Prioritize strategies to improve model performance
• Handle complex scenarios like mismatched data distributions
• Apply transfer and multi-task learning appropriately
• Set effective goals and evaluation metrics for ML projects