Debugging 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.

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Spis treści

Debugging Machine Learning Models with Python. Develop high-performance, low-bias, and explainable machine learning and deep learning models eBook -- spis treści

• 1. Beyond Code Debugging
• 2. Machine Learning Life Cycle
• 3. Debugging toward Responsible AI
• 4. Detecting Performance and Efficiency Issues in Machine Learning Models
• 5. Improving the Performance of Machine Learning Models
• 6. Interpretability and Explainability in Machine Learning Modeling
• 7. Decreasing Bias and Achieving Fairness
• 8. Controlling Risks Using Test-Driven Development
• 9. Testing and Debugging for Production
• 10. Versioning and Reproducible Machine Learning Modeling
• 11. Avoiding and Detecting Data and Concept Drifts
• 12. Going Beyond ML Debugging with Deep Learning
• 13. Advanced Deep Learning Techniques
• 14. Introduction to Recent Advancements in Machine Learning
• 15. Correlation versus Causality
• 16. Security and Privacy in Machine Learning
• 17. Human-in-the-Loop Machine Learning