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Debugging Machine Learning Models with Python
Debugging Machine Learning Models with Python

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

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Debugging Machine Learning Models with Python

Beyond Code Debugging

Artificial intelligence (AI), like human intelligence, is a capability and tool that can be used for decision-making and task accomplishment. As humans, we use our intelligence in making our daily decisions and thinking about the challenges and problems we deal with. We use our brains and central nervous systems to receive information from our surroundings and process them for decision-making and reactions.

Machine learning models are the AI techniques that are used nowadays to tackle problems across healthcare and finance. Machine learning models have been used in robotic systems in manufacturing facilities to package products or identify products that might have been damaged. They have been used in our smartphones to identify our faces for security purposes, by e-commerce companies to suggest the most suited products or movies to us, and even for improving healthcare and drug development to bring new more effective drugs onto the market for severe diseases.

In this chapter, we will provide a quick review of different types of machine learning modeling. You will learn about different techniques and challenges in debugging your machine learning code. We will also discuss why debugging machine learning modeling goes far beyond just code debugging.

We will cover the following topics in this chapter:

  • Machine learning at a glance
  • Types of machine learning modeling
  • Debugging in software development
  • Flaws in data used for modeling
  • Model and prediction-centric debugging

This chapter is an introduction to this book to prepare you for more advanced concepts that will be presented later. This will help you improve your models and move toward becoming an expert in the machine learning era.

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date : Sep 15, 2023
Length: 344 pages
Edition : 1st
Language : English
ISBN-13 : 9781800201132
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Product Details

Publication date : Sep 15, 2023
Length: 344 pages
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Table of Contents

25 Chapters
Part 1:Debugging for Machine Learning Modeling Chevron down icon Chevron up icon
Chapter 1: Beyond Code Debugging Chevron down icon Chevron up icon
Chapter 2: Machine Learning Life Cycle Chevron down icon Chevron up icon
Chapter 3: Debugging toward Responsible AI Chevron down icon Chevron up icon
Part 2:Improving Machine Learning Models Chevron down icon Chevron up icon
Chapter 4: Detecting Performance and Efficiency Issues in Machine Learning Models Chevron down icon Chevron up icon
Chapter 5: Improving the Performance of Machine Learning Models Chevron down icon Chevron up icon
Chapter 6: Interpretability and Explainability in Machine Learning Modeling Chevron down icon Chevron up icon
Chapter 7: Decreasing Bias and Achieving Fairness Chevron down icon Chevron up icon
Part 3:Low-Bug Machine Learning Development and Deployment Chevron down icon Chevron up icon
Chapter 8: Controlling Risks Using Test-Driven Development Chevron down icon Chevron up icon
Chapter 9: Testing and Debugging for Production Chevron down icon Chevron up icon
Chapter 10: Versioning and Reproducible Machine Learning Modeling Chevron down icon Chevron up icon
Chapter 11: Avoiding and Detecting Data and Concept Drifts Chevron down icon Chevron up icon
Part 4:Deep Learning Modeling Chevron down icon Chevron up icon
Chapter 12: Going Beyond ML Debugging with Deep Learning Chevron down icon Chevron up icon
Chapter 13: Advanced Deep Learning Techniques Chevron down icon Chevron up icon
Chapter 14: Introduction to Recent Advancements in Machine Learning Chevron down icon Chevron up icon
Part 5:Advanced Topics in Model Debugging Chevron down icon Chevron up icon
Chapter 15: Correlation versus Causality Chevron down icon Chevron up icon
Chapter 16: Security and Privacy in Machine Learning Chevron down icon Chevron up icon
Chapter 17: Human-in-the-Loop Machine Learning Chevron down icon Chevron up icon
Assessments Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(16 Ratings)
5 star 93.8%
4 star 6.3%
3 star 0%
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Amazon Customer Mar 25, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
What I like about it the most is that even though it’s comprehensive, accurate and detailed, it’s easy tofollow and a pleasure to read.I’d recommend this to data scientists, machine learning engineers, developers, and students eager to refine their skills in crafting production-ready, ethical, and explainable ML models.
Amazon Verified review Amazon
Mehrdad Mastali Feb 28, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is an essential guide for data scientists on the machine learning lifecycle, offering practical insights for developing reliable and high-performance models. Its clear explanations, real-world examples, and focus on the best practices make it invaluable for both beginners and experienced professionals seeking to enhance their ML projects.
Amazon Verified review Amazon
Pooya Mirzabeygi Oct 12, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is what I've been looking for so long! I've read many machine learning books and often got caught up in some complex math details that discouraged me from moving forward. This book however, really starts from the basic fundamentals and takes you to advanced concepts with real-world examples and simple language in a clear path. It also helped me develop a better understanding of machine learning life cycle, model performance and techniques and all you really need to start is some basic understanding of Python. I recommend it to everyone from the beginners to professionals in the field.
Amazon Verified review Amazon
H2N Oct 26, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a goldmine for data enthusiasts, ranging from analysts to Python aficionados. Diving deep into machine learning, it marries foundational knowledge with advanced topics, from code debugging and ML life cycles to deep learning and human-centric approaches. With an emphasis on practical examples, readers are guided through vital areas like model performance, fairness, and security. Catering to both beginners and experts in the ML realm, this book seamlessly blends basic insights with intricate techniques, making it a must-read for all.
Amazon Verified review Amazon
pari Feb 10, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great resource for anyone looking to enhance their understanding of debugging in the context of machine learning.Clear explanations and practical examples. Highly recommended!
Amazon Verified review Amazon
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