Structuring Machine Learning Projects is a practical, project-focused training program designed to help data scientists, machine learning practitioners, and technical professionals plan, organize, develop, evaluate, and deliver successful machine learning projects. The course focuses on the complete machine learning project lifecycle, helping participants move beyond individual modeling techniques and develop a structured approach to solving real-world problems.
Course Overview:
Structuring Machine Learning Projects is an advanced, strategy-focused training program designed to help machine learning professionals develop the skills needed to plan, prioritize, diagnose, and improve real-world machine learning projects. Rather than focusing primarily on individual algorithms, the course teaches participants how to make effective technical decisions throughout the ML project lifecycle and determine which improvements are most likely to deliver meaningful results.
Participants will learn practical strategies for evaluating machine learning systems using single-number evaluation metrics, training/development/test set design, human-level performance benchmarks, and error analysis. These techniques help teams identify the most important sources of model errors and prioritize improvements based on measurable impact.
The course addresses complex machine learning environments, including situations involving mismatched training and test distributions, data quality problems, incorrectly labeled data, bias and variance, and changing evaluation requirements. Participants will learn how to recognize these challenges and select appropriate strategies for improving model performance.
A major focus of the training is learning how to build an effective ML system quickly and improve it through structured iteration. Participants will explore approaches such as data cleaning, error analysis, transfer learning, multi-task learning, and end-to-end deep learning, while learning when each approach is appropriate and what trade-offs should be considered.
Through practical exercises and decision-making simulations, learners will experience scenarios that mirror the types of technical choices faced by ML project leaders. These activities help participants develop the ability to set priorities, evaluate competing solutions, communicate technical direction, and make data-driven decisions when improving machine learning systems.
By the end of the course, participants will be able to structure ML projects more effectively, diagnose model errors, prioritize improvement strategies, design appropriate data splits and evaluation metrics, address data mismatch, and determine when advanced approaches such as transfer learning, multi-task learning, or end-to-end learning should be applied.
Course Objectives:
- Understand how to diagnose errors in a machine learning system, and
- Be able to prioritize the most promising directions for reducing error
- Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
- Know how to apply end-to-end learning, transfer learning, and multi-task learning
Pre-requisites:
This course is aimed at individuals with basic knowledge of machine learning, who want to know how to set technical direction and prioritization for their work. – It is recommended that you take course one and two of this specialization (Neural Networks and Deep Learning, and Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization) prior to beginning this course.
Target Audience:
This course is designed for professionals who already have a foundation in machine learning and want to strengthen their ability to lead, structure, evaluate, and improve machine learning projects.
- Machine Learning Engineers responsible for developing and improving production ML systems
- AI Engineers working on practical AI and deep learning solutions
- Data Scientists seeking to improve their approach to model evaluation and project strategy
- Machine Learning Researchers working on model performance, experimentation, and applied research
- Data Mining and Analytics Professionals applying machine learning to complex datasets
- Business Intelligence (BI) Developers expanding their skills into machine learning and AI
- Technical Leads and AI Project Leaders responsible for setting technical direction and prioritizing ML initiatives
- Software Engineers transitioning into AI/ML roles who want to develop stronger project-level decision-making skills
- Experienced ML Practitioners looking to improve their ability to diagnose errors and optimize ML project outcomes
Course Duration:
- 35 hours – 5 days
Course Content:
ML Strategy 1
- Why ML Strategy
- Orthogonalization
- Single number evaluation metric
- Satisfying and Optimizing metric
- Train/Dev/Test distributions
- Size of the Dev and Test sets
- When to change Dev/Test sets and metrics
- Why human-level performance?
- Avoidable bias
- Understanding human-level performance
- Surpassing human-level performance
- Improving your model performance
ML Strategy 2
- Carrying out error analysis
- Cleaning up incorrectly labeled data
- Build your first system quickly, then iterate
- Training and testing on different distributions
- Bias and Variance with mismatched data distributions
- Addressing data mismatch
- Transfer learning
- Multi-task learning
- What is end-to-end deep learning?
- Whether to use end-to-end deep learning
