Machine Learning Pitfalls - A Brief Guide on How to Avoid Common Pitfalls (With Code Samples)
Machine Learning Pitfalls - A Brief Guide on How to Avoid Common Pitfalls (With Code Samples)
About the Book
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.
The paperback is available on Amazon: Machine Learning Pitfalls - A Brief Guide on How to Avoid Common Pitfalls (With Code Samples)
About the Author
Table of Contents
- Overview
- Why machine learning is prone to pitfalls
- The importance of avoiding pitfalls
- The importance of high-quality data
- Common issues in data collection and preparation
- Strategies for overcoming data-related pitfalls
- 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
- The importance of selecting and engineering the right features
- Common pitfalls in feature selection and engineering
- Strategies for avoiding feature-related pitfalls
- 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
- 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
- The importance of deploying models carefully
- Common pitfalls in model deployment and monitoring
- Strategies for ensuring model reliability and stability
- The ethical implications of machine learning
- How to address ethical concerns in machine learning
- Best practices for ethical machine learning
- 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
- Design
- Development
- Interpret and Communicate
- Deployment
- Threats to data
- Differential privacy
- Distributed and Federated Learning
- Training over encrypted data.
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