Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models

by Ali Madani, Stephen MacKinnon (Foreword by)

Overview

Debugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies.
By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.

Product Details

ISBN-13:	9781800201132
Publisher:	Packt Publishing
Publication date:	09/15/2023
Sold by:	Barnes & Noble
Format:	eBook
Pages:	344
File size:	10 MB

About the Author

Ali Madani worked as the director of machine learning at Cyclica Inc leading AI technology development front of Cyclica for drug discovery before acquisition of Cyclica by Recursion Pharmaceuticals. Ali completed his PhD at University of Toronto focusing on machine learning modeling in cancer setting and attained a Master of Mathematics from the University of Waterloo. As a believer in industry-oriented education and pro-democratization of knowledge, Ali has actively educated students and professionals through international workshops and courses on basic and advanced high-quality machine learning modeling. When not immersed in machine learning modeling and teaching, Ali enjoys exercising, cooking and traveling with his partner.

Table of Contents

Table of Contents

1. Beyond Code Debugging
2. Machine Learning Life Cycle
3. Debugging toward Responsible AI
4. Detecting Performance and Efficiency Issues in Machine Learning Models
5. Improving the Performance of Machine Learning Models
6. Interpretability and Explainability in Machine Learning Modeling
7. Decreasing Bias and Achieving Fairness
8. Controlling Risks Using Test-Driven Development
9. Testing and Debugging for Production
10. Versioning and Reproducible Machine Learning Modeling
11. Avoiding and Detecting Data and Concept Drifts
12. Going Beyond ML Debugging with Deep Learning
13. Advanced Deep Learning Techniques
14. Introduction to Recent Advancements in Machine Learning
15. Correlation versus Causality
16. Security and Privacy in Machine Learning
17. Human-in-the-Loop Machine Learning