Debugging Machine Learning Models with Python

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

17 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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Debugging machine learning systems is a critical skill for building reliable, trustworthy, and high-performing AI solutions. This course teaches you how to identify, diagnose, and resolve issues throughout the machine learning lifecycle, helping you create models that are accurate, efficient, explainable, and production-ready. You will learn practical techniques to evaluate model behavior, improve performance, detect bias, manage risks, and implement testing strategies for machine learning applications. Through hands-on exploration of Python-based workflows, you will develop the ability to build reproducible pipelines, address data and concept drift, and strengthen model reliability in real-world environments. Unlike courses that focus only on model development, this course emphasizes systematic debugging and responsible AI practices. It combines foundational machine learning concepts with advanced topics such as deep learning, explainability, causality, security, privacy, and human-in-the-loop machine learning to bridge the gap between theory and industrial deployment. This course is ideal for data scientists, machine learning engineers, analysts, AI practitioners, and Python developers seeking to improve model quality and operational excellence. Learners should have basic Python programming knowledge and familiarity with machine learning concepts; the course is designed at an intermediate level.

Syllabus

Beyond Code Debugging

This module guides learners through advanced debugging techniques in machine learning, focusing on identifying data flaws and improving model reliability. Participants will explore different types of machine learning models, learn to interpret error messages, and apply strategies for debugging both code and model predictions.

Machine Learning Life Cycle

This module guides learners through the complete machine learning workflow, from data collection and preprocessing to model evaluation and deployment. Participants will gain practical skills in data wrangling, handling missing values, scaling features, and designing robust testing strategies. By the end, learners will understand how to structure and execute a machine learning project in real-world settings.

Debugging toward Responsible AI

This module introduces key principles and practices for developing responsible AI systems, focusing on fairness, security, transparency, and accountability. Learners will examine sources of bias, explore privacy and integrity challenges, and discover strategies for building trustworthy machine learning models.

Detecting Performance and Efficiency Issues in Machine Learning Models

This module guides learners through evaluating machine learning models using key performance metrics, visualization techniques, and validation strategies. You will explore how to diagnose bias and variance, assess clustering results, and conduct error analysis to identify and address efficiency issues. By the end, you'll be equipped to systematically improve model performance and reliability.

Improving the Performance of Machine Learning Models

This module introduces practical strategies to boost the effectiveness and generalizability of machine learning models. Learners will explore data augmentation, hyperparameter tuning, and regularization, as well as techniques for handling limited or lower-quality data. By the end, you'll be equipped to enhance model performance through improved data processing and optimization methods.

Interpretability and Explainability in Machine Learning Modeling

This module introduces key concepts and techniques for making machine learning models more transparent and understandable. Learners will explore both local and global explainability methods, including hands-on practice with SHAP and counterfactual analysis in Python. By the end, you'll be able to interpret model predictions and assess feature contributions to improve model trustworthiness.

Decreasing Bias and Achieving Fairness

This module introduces key concepts and practical tools for reducing bias and promoting fairness in machine learning models. Learners will examine sources of bias, explore fairness metrics, and utilize Python libraries to assess and improve model fairness in real-world scenarios.

Controlling Risks Using Test-Driven Development

This module introduces strategies to mitigate risks in machine learning projects by leveraging test-driven development, differential testing, and experiment tracking. Learners will discover how to use tools like Pytest fixtures to streamline testing and ensure model reliability. The module also covers best practices for documenting and tracking experiments to support robust, reproducible results.

Testing and Debugging for Production

This module introduces essential strategies for ensuring machine learning models perform reliably in production environments. Learners will explore integration testing of ML pipelines, infrastructure testing, and techniques for monitoring and validating live model performance using Python tools. Emphasis is placed on maintaining model quality and detecting issues post-deployment.

Versioning and Reproducible Machine Learning Modeling

This module introduces the principles and practices of ensuring reproducibility in machine learning projects by leveraging data and model versioning. Learners will discover how effective version control enhances collaboration, traceability, and reliability throughout the machine learning pipeline.

Avoiding and Detecting Data and Concept Drifts

This module delves into the challenges of data and concept drift in machine learning, highlighting their impact on model reliability. Learners will gain hands-on experience using Python libraries such as Alibi Detect and Evidently to identify and address drifts, ensuring robust model performance.

Going Beyond ML Debugging with Deep Learning

This module introduces the fundamentals of deep learning and the PyTorch framework, emphasizing neural network construction and practical model development. Learners will explore key optimization algorithms and discover how hyperparameter tuning can enhance model performance. By the end, you'll gain hands-on insights into building and refining deep learning models.

Advanced Deep Learning Techniques

This module delves into advanced deep learning methods for handling images, text, and graph data using CNNs, transformers, and GNNs in PyTorch. Learners will gain practical experience with data preprocessing, model development, and leveraging pre-trained models for various data types. The module emphasizes hands-on techniques for transforming and augmenting data to improve model performance.

Introduction to Recent Advancements in Machine Learning

This module introduces cutting-edge developments in machine learning, focusing on generative modeling, reinforcement learning, and self-supervised learning. Learners will explore practical applications, including prompt engineering and PyTorch implementations, to understand how these advancements are shaping modern AI.

Correlation versus Causality

This module delves into the critical distinction between correlation and causality in machine learning, highlighting why understanding causation is essential for building reliable models. Learners will explore causal modeling techniques and gain hands-on experience with Python libraries such as DoWhy and bnlearn to perform causal inference and reduce bias in their analyses.

Security and Privacy in Machine Learning

This module introduces key techniques for safeguarding machine learning systems and user data, including encryption methods, differential privacy, and federated learning. Learners will gain foundational knowledge of how these approaches enhance security and privacy in real-world machine learning applications.

Human-in-the-Loop Machine Learning

This module introduces the concept of integrating human feedback into machine learning workflows to improve model accuracy and reliability. Learners will discover how domain experts and non-experts contribute to the iterative development of machine learning systems in practical settings.