Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: Debugging for Machine Learning Modeling -- Chapter 1: Beyond Code Debugging -- Technical requirements -- Machine learning at a glance -- Types of machine learning modeling -- Supervised learning -- Unsupervised learning -- Self-supervised learning -- Semi-supervised learning -- Reinforcement learning -- Generative machine learning -- Debugging in software development -- Error messages in Python -- Debugging techniques -- Debuggers -- Best practices for high-quality Python programming -- Version control -- Debugging beyond Python -- Flaws in data used for modeling -- Data format and structure -- Data quantity and quality -- Data biases -- Model and prediction-centric debugging -- Underfitting and overfitting -- Inference in model testing and production -- Data or hyperparameters for changing landscapes -- Summary -- Questions -- References -- Chapter 2: Machine Learning Life Cycle -- Technical requirements -- Before we start modeling -- Data collection -- Data selection -- Data exploration -- Data wrangling -- Structuring -- Enriching -- Data transformation -- Cleaning -- Modeling data preparation -- Feature selection and extraction -- Designing an evaluation and testing strategy -- Model training and evaluation -- Testing the code and the model -- Model deployment and monitoring -- Summary -- Questions -- References -- Chapter 3: Debugging toward Responsible AI -- Technical requirements -- Impartial modeling fairness in machine learning -- Data bias -- Algorithmic bias -- Security and privacy in machine learning -- Data privacy -- Data poisoning -- Adversarial attacks -- Output integrity attacks -- System manipulation -- Secure and private machine learning techniques -- Transparency in machine learning modeling…”

“…Debugging application performance…”