Debugging Machine Learning Models with Python
Develop high-performance, low-bias, and explainable machine learning and deep learning models
Published on 15. September 2023
Book
Paperback/Softback
344 pages
978-1-80020-858-2 (ISBN)
Description
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 DescriptionDebugging 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 forThis 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.
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 DescriptionDebugging 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 forThis 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.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 19 mm
Weight
643 gr
ISBN-13
978-1-80020-858-2 (9781800208582)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Ali Madani
Debugging Machine Learning Models with Python
Develop high-performance, low-bias, and explainable machine learning and deep learning models
E-Book
05/2024
1st Edition
Packt Publishing Limited
from
€35.99
Available for download
Person
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.
Content
Table of Contents
Beyond Code Debugging
Machine Learning Life Cycle
Debugging toward Responsible AI
Detecting Performance and Efficiency Issues in Machine Learning Models
Improving the Performance of Machine Learning Models
Interpretability and Explainability in Machine Learning Modeling
Decreasing Bias and Achieving Fairness
Controlling Risks Using Test-Driven Development
Testing and Debugging for Production
Versioning and Reproducible Machine Learning Modeling
Avoiding and Detecting Data and Concept Drifts
Going Beyond ML Debugging with Deep Learning
Advanced Deep Learning Techniques
Introduction to Recent Advancements in Machine Learning
Correlation versus Causality
Security and Privacy in Machine Learning
Human-in-the-Loop Machine Learning
Beyond Code Debugging
Machine Learning Life Cycle
Debugging toward Responsible AI
Detecting Performance and Efficiency Issues in Machine Learning Models
Improving the Performance of Machine Learning Models
Interpretability and Explainability in Machine Learning Modeling
Decreasing Bias and Achieving Fairness
Controlling Risks Using Test-Driven Development
Testing and Debugging for Production
Versioning and Reproducible Machine Learning Modeling
Avoiding and Detecting Data and Concept Drifts
Going Beyond ML Debugging with Deep Learning
Advanced Deep Learning Techniques
Introduction to Recent Advancements in Machine Learning
Correlation versus Causality
Security and Privacy in Machine Learning
Human-in-the-Loop Machine Learning