Interpretability And Explainability In Ai Using Python Decrypt Ai Decision Making Using Interpretability And Explainability With Python To Build Reliable Machine Learning Systems
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Interpretability And Explainability In Ai Using Python Decrypt Ai Decision Making Using Interpretability And Explainability With Python To Build Reliable Machine Learning Systems
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Author : Aruna Chakkirala
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-04-15
Interpretability And Explainability In Ai Using Python Decrypt Ai Decision Making Using Interpretability And Explainability With Python To Build Reliable Machine Learning Systems written by Aruna Chakkirala and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
Demystify AI Decisions and Master Interpretability and Explainability Today Key Features● Master Interpretability and Explainability in ML, Deep Learning, Transformers, and LLMs● Implement XAI techniques using Python for model transparency● Learn global and local interpretability with real-world examples Book DescriptionInterpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust. Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models. You’ll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you’ll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals—powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems. Through hands-on Python examples, you’ll learn how to apply these techniques in real-world scenarios. By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards—giving you a competitive edge in the evolving AI landscape. What you will learn● Dissect key factors influencing model interpretability and its different types.● Apply post-hoc and inherent techniques to enhance AI transparency.● Build explainable AI (XAI) solutions using Python frameworks for different models.● Implement explainability methods for deep learning at global and local levels.● Explore cutting-edge research on transparency in transformers and LLMs.● Learn the role of XAI in Responsible AI, including key tools and methods.
Interpretability And Explainability In Ai Using Python
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Author : Aruna Chakkirala
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-04-15
Interpretability And Explainability In Ai Using Python written by Aruna Chakkirala and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
TAGLINE Demystify AI Decisions and Master Interpretability and Explainability Today KEY FEATURES ● Master Interpretability and Explainability in ML, Deep Learning, Transformers, and LLMs ● Implement XAI techniques using Python for model transparency ● Learn global and local interpretability with real-world examples DESCRIPTION Interpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust. Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models. You’ll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you’ll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals—powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems. Through hands-on Python examples, you’ll learn how to apply these techniques in real-world scenarios. By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards—giving you a competitive edge in the evolving AI landscape. WHAT WILL YOU LEARN ● Dissect key factors influencing model interpretability and its different types. ● Apply post-hoc and inherent techniques to enhance AI transparency. ● Build explainable AI (XAI) solutions using Python frameworks for different models. ● Implement explainability methods for deep learning at global and local levels. ● Explore cutting-edge research on transparency in transformers and LLMs. ● Learn the role of XAI in Responsible AI, including key tools and methods. WHO IS THIS BOOK FOR? This book is tailored for Machine Learning Engineers, AI Engineers, and Data Scientists working on AI applications. It also serves as a valuable resource for professionals and students in AI-related fields looking to enhance their expertise in model interpretability and explainability techniques. TABLE OF CONTENTS 1. Interpreting Interpretable Machine Learning 2. Model Types and Interpretability Techniques 3. Interpretability Taxonomy and Techniques 4. Feature Effects Analysis with Plots 5. Post-Hoc Methods 6. Anchors and Counterfactuals 7. Interpretability in Neural Networks 8. Explainable Neural Networks 9. Explainability in Transformers and Large Language Models 10. Explainability and Responsible AI Index
Practical Explainable Ai Using Python
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Author : Pradeepta Mishra
language : en
Publisher:
Release Date : 2022
Practical Explainable Ai Using Python written by Pradeepta Mishra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data and natural language processing-related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks. You will: Review the different ways of making an AI model interpretable and explainable Examine the biasness and good ethical practices of AI models Quantify, visualize, and estimate reliability of AI models Design frameworks to unbox the black-box models Assess the fairness of AI models Understand the building blocks of trust in AI models Increase the level of AI adoption.
Explainable Ai With Python
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Author : Antonio Di Cecco
language : en
Publisher: Springer Nature
Release Date : 2025-08-04
Explainable Ai With Python written by Antonio Di Cecco and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-04 with Computers categories.
This comprehensive book on Explainable Artificial Intelligence has been updated and expanded to reflect the latest advancements in the field of XAI, enriching the existing literature with new research, case studies, and practical techniques. The Second Edition expands on its predecessor by addressing advancements in AI, including large language models and multimodal systems that integrate text, visual, auditory, and sensor data. It emphasizes making complex systems interpretable without sacrificing performance and provides an enhanced focus on additive models for improved interpretability. Balancing technical rigor with accessibility, the book combines theory and practical application to equip readers with the skills needed to apply explainable AI (XAI) methods effectively in real-world contexts. Features: Expansion of the "Intrinsic Explainable Models" chapter to delve deeper into generalized additive models and other intrinsic techniques, enriching the chapter with new examples and use cases for a better understanding of intrinsic XAI models. Further details in "Model-Agnostic Methods for XAI" focused on how explanations differ between the training set and the test set, including a new model to illustrate these differences more clearly and effectively. New section in "Making Science with Machine Learning and XAI" presenting a visual approach to learning the basic functions in XAI, making the concept more accessible to readers through an interactive and engaging interface. Revision in "Adversarial Machine Learning and Explainability" that includes a code review to enhance understanding and effectiveness of the concepts discussed, ensuring that code examples are up-to-date and optimized for current best practices. New chapter on "Generative Models and Large Language Models (LLM)" chapter dedicated to generative models and large language models, exploring their role in XAI and how they can be used to create richer, more interactive explanations. This chapter also covers the explainability of transformer models and privacy through generative models. New "Artificial General Intelligence and XAI" mini-chapter dedicated to exploring the implications of Artificial General Intelligence (AGI) for XAI, discussing how advancements towards AGI systems influence strategies and methodologies for XAI. Enhancements in "Explaining Deep Learning Models" features new methodologies in explaining deep learning models, further enriching the chapter with cutting-edge techniques and insights for deeper understanding.
Explainable Ai Recipes
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Author : Pradeepta Mishra
language : en
Publisher:
Release Date : 2023
Explainable Ai Recipes written by Pradeepta Mishra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. You will: Create code snippets and explain machine learning models using Python Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale Understand the different variants of neural network models.
Interpretable Machine Learning With Python
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Author : Serg Masís
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-10-31
Interpretable Machine Learning With Python written by Serg Masís and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-31 with Computers categories.
A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods Analyze and extract insights from complex models from CNNs to BERT to time series models Book DescriptionInterpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.What you will learn Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers Use monotonic and interaction constraints to make fairer and safer models Understand how to mitigate the influence of bias in datasets Leverage sensitivity analysis factor prioritization and factor fixing for any model Discover how to make models more reliable with adversarial robustness Who this book is for This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.
Hands On Explainable Ai Xai With Python
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-31
Hands On Explainable Ai Xai With Python written by Denis Rothman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with Computers categories.
Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications
Ethical Ai Responsible Machine Learning With Python
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Author : Pythquill Publishing
language : en
Publisher: Independently Published
Release Date : 2025-07
Ethical Ai Responsible Machine Learning With Python written by Pythquill Publishing and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07 with Computers categories.
What You Will Learn in This Book Understand the Principles of Ethical AI: Learn why ethics is critical in AI development and how principles like fairness, transparency, accountability, and privacy can be practically applied to machine learning systems. Identify and Address Bias in Machine Learning: Discover the different sources and types of bias in datasets and models, and gain hands-on skills to detect, measure, and interpret bias using Python-based tools and metrics. Apply Fairness Metrics to Evaluate ML Models: Learn how to choose and implement fairness metrics such as demographic parity, equal opportunity, and predictive equality to assess the ethical impact of your models. Implement Bias Mitigation Techniques: Explore proven pre-processing, in-processing, and post-processing strategies to reduce unfairness in machine learning systems, and apply them using tools like AIF360 and Fairlearn. Build Explainable AI Systems: Understand the importance of explainability in AI and how to make machine learning models more interpretable using both model-specific and model-agnostic techniques with libraries such as LIME and SHAP. Visualize and Communicate Model Behavior: Gain practical experience generating visual explanations and summaries that help stakeholders understand model decisions, improve trust, and meet compliance standards. Strengthen ML Privacy and Security: Learn how to protect user data and mitigate privacy risks by implementing techniques like differential privacy, federated learning, and homomorphic encryption in your ML workflows. Evaluate and Defend Against Adversarial Threats: Understand common adversarial attacks on machine learning models and apply countermeasures to improve model robustness using Python libraries such as ART and CleverHans. Design Accountable AI Workflows: Discover how to create audit-ready documentation artifacts like model cards and datasheets, and incorporate traceability and reproducibility into your development pipeline. Integrate Ethics into MLOps Pipelines: Learn how to operationalize responsible AI practices by embedding fairness, explainability, and privacy checks into continuous integration and deployment (CI/CD) systems. Monitor and Maintain Ethical AI in Production: Develop strategies for tracking model performance and fairness over time, detecting ethical drift, and retraining models responsibly as data evolves. Foster Responsible AI Culture in Organizations: Explore how diverse teams, ethical review boards, and clear communication practices can help build a sustainable and accountable AI development culture. Apply Python to Real-World Responsible AI Projects: Work through end-to-end case studies that apply responsible AI principles to real-world scenarios in finance, healthcare, recommender systems, and NLP. Stay Informed on AI Ethics Trends and Regulations: Gain awareness of current and emerging global AI regulations, ethical frameworks, and industry standards that impact how AI systems are built and governed. Build a Long-Term Ethical AI Skillset: Equip yourself with tools, resources, and best practices to continue learning and adapting in the rapidly evolving field of ethical AI and responsible machine learning.
Interpretable Machine Learning With Python
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Author : Serg Masís
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-03-26
Interpretable Machine Learning With Python written by Serg Masís and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with Computers categories.
A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.
Debugging Machine Learning Models With Python
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Author : Ali Madani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-09-15
Debugging Machine Learning Models With Python written by Ali Madani and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-15 with Computers categories.
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.