Machine Learning Pitfalls: A Brief Guide on How to Avoid Common Pitfalls (With Code Samples)

By Murat Durmus

This book will be a helpful resource for anyone interested in avoiding the pitfalls of machine learning and building trustworthy models. Whether you are a seasoned machine learning practitioner or a newcomer to the field, the lessons in this book will be valuable to you. About the Author: Murat Durmus is CEO and founder of AISOMA (a Frankfurt am Main (Germany) based company specializing in AI-based technology development and consulting) and Author of the books "Mindful AI - Reflections on Artificial Intelligence" & "A Primer to the 42 Most commonly used Machine Learning Algorithms (With Code Samples." Table of Contents: Introduction Overview Why machine learning is prone to pitfalls The importance of avoiding pitfalls Data Collection and Preparation The importance of high-quality data Common issues in data collection and preparation Strategies for overcoming data-related pitfalls Model Selection and Evaluation Understanding different types of models The importance of choosing the right model How to evaluate model performance Overfitting and Underfitting The dangers of overfitting and underfitting How to detect and avoid overfitting and underfitting Feature Selection and Engineering The importance of selecting and engineering the right features Common pitfalls in feature selection and engineering Strategies for avoiding feature-related pitfalls Bias and Fairness Common pitfalls in bias and fairness include: Strategies for avoiding bias and ensuring fairness include: Understanding bias and fairness in machine learning Common sources of bias and unfairness How to detect and mitigate bias and unfairness Interpretability and Explainability Common techniques for improving interpretability and explainability include: Why interpretability and explainability are important Common challenges in building interpretable and explainable models Strategies for improving interpretability and explainability Deployment and Monitoring The importance of deploying models carefully Common pitfalls in model deployment and monitoring Strategies for ensuring model reliability and stability Ethical Considerations The ethical implications of machine learning How to address ethical concerns in machine learning Best practices for ethical machine learning Conclusion Recap of key lessons Future directions for avoiding machine learning pitfalls. Hands-on Code Examples Using LIME to explain the decision of a classification model Ensuring the robustness of machine learning models Prevent overfitting using regularization techniques How to diagnose and address underfitting in Python How to address class imbalance in Python How to address feature selection bias Increasing the explainability of machine learning models Mitigating bias in machine learning models Fairness checking Ensuring the safety of machine learning models Typical Stages of Machine Learning Lifecycle Design Development Interpret and Communicate Deployment Data Ethics: A Checklist with 7 Points to Consider Privacy in AI systems Threats to data Differential privacy Distributed and Federated Learning Training over encrypted data.

• Format: Paperback
• Language: English
• ISBN: B0BXMZ19YB
• ISBN13: 9798386789114
• Release Date: March 2023
• Publisher: Independently published
• Length: 129 Pages
• Dimensions: 9.0" x 0.3" x 6.0"