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

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  • Categories

    • Ethics & Technology
    • Artificial Intelligence
    • Machine Learning
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Table of Contents

  •    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.

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