Have a personal or library account? Click to login
Debugging Machine Learning Models with Python Cover

Debugging Machine Learning Models with Python

Develop high-performance, low-bias, and explainable machine learning and deep learning models

Publisher:Packt Publishing Limited
By:
Paid access
|Sep 2023

Master reproducible ML and DL models with Python and PyTorch to achieve high performance, explainability, and real-world success

Key Features

  • Learn how to improve performance of your models and eliminate model biases
  • Strategically design your machine learning systems to minimize chances of failure in production
  • Discover advanced techniques to solve real-world challenges
  • Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

  • Enhance data quality and eliminate data flaws
  • Effectively assess and improve the performance of your models
  • Develop and optimize deep learning models with PyTorch
  • Mitigate biases to ensure fairness
  • Understand explainability techniques to improve model qualities
  • Use test-driven modeling for data processing and modeling improvement
  • Explore techniques to bring reliable models to production
  • Discover the benefits of causal and human-in-the-loop modeling

Who this book is for

This book is for data scientists, analysts, machine learning engineers, Python developers, and students looking to build reliable, high-performance, and explainable machine learning models for production across diverse industrial applications. Fundamental Python skills are all you need to dive into the concepts and practical examples covered. Whether you're new to machine learning or an experienced practitioner, this book offers a breadth of knowledge and practical insights to elevate your modeling skills.

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
https://github.com/PacktPublishing/Debugging-Machine-Learning-Models-with-Python
PDF ISBN: 978-1-80020-113-2
Publisher: Packt Publishing Limited
Copyright owner: © 2023 Packt Publishing Limited
Publication date: 2023
Language: English
Pages: 344
Related subjects:
Computer sciences,
Artificial intelligence

People also read