Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models

Master reproducible ML and DL models with Python and PyTorch to achieve high performance, explainability, and real-world success

Key Features:

• Learn how to improve performance of your models and eliminate model biases
• Strategically design your machine learning systems to minimize chances of failure in production
• Discover advanced techniques to solve real-world challenges
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description:

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.

What You Will Learn:

• Enhance data quality and eliminate data flaws
• Effectively assess and improve the performance of your models
• Develop and optimize deep learning models with PyTorch
• Mitigate biases to ensure fairness
• Understand explainability techniques to improve model qualities
• Use test-driven modeling for data processing and modeling improvement
• Explore techniques to bring reliable models to production
• Discover the benefits of causal and human-in-the-loop modeling

Who this book is for:

This book is for data scientists, analysts, machine learning engineers, Python developers, and students looking to build reliable, high-performance, and explainable machine learning models for production across diverse industrial applications. Fundamental Python skills are all you need to dive into the concepts and practical examples covered. Whether you're new to machine learning or an experienced practitioner, this book offers a breadth of knowledge and practical insights to elevate your modeling skills.

以 Python 和 PyTorch 掌握可重複的機器學習和深度學習模型，實現高性能、可解釋性和真實世界的成功

主要特點：
- 學習如何提高模型的性能並消除模型偏見
- 策略性地設計機器學習系統，以減少在生產中失敗的機會
- 探索解決真實世界挑戰的高級技術
- 購買印刷版或 Kindle 版本的書籍將包含免費的 PDF 電子書

書籍描述：
《使用 Python 調試機器學習模型》是一本全面的指南，引導您從基礎概念到高級技術的整個機器學習之旅。它超越基礎知識，為您提供在工業應用中構建可靠、高性能模型所必需的專業知識。無論您是數據科學家、分析師、機器學習工程師還是 Python 開發人員，本書都將使您能夠設計模塊化的數據準備系統，準確地訓練和測試模型，並將其無縫集成到更大的技術中。

通過將理論與實踐相結合，您將學習如何評估模型性能，識別和解決問題，並利用 PyTorch 和 scikit-learn 中的深度學習和生成建模的最新進展。您的實踐高質量模型之旅還將涵蓋因果和人為參與建模以及機器學習的可解釋性。通過實際示例和清晰的解釋，您將開發出在醫療保健、金融和電子商務等領域提供有影響力的解決方案的能力。

您將學到什麼：
- 提高數據質量並消除數據缺陷
- 有效評估和改進模型的性能
- 使用 PyTorch 開發和優化深度學習模型
- 減輕偏見以確保公平性
- 理解提高模型質量的可解釋性技術
- 使用測試驅動的建模進行數據處理和模型改進
- 探索將可靠模型引入生產的技術
- 發現因果和人為參與建模的好處

本書適合對象：
本書適合數據科學家、分析師、機器學習工程師、Python 開發人員和學生，他們希望在各種工業應用中構建可靠、高性能和可解釋的機器學習模型。只需具備基本的 Python 技能，即可深入研究所涵蓋的概念和實際示例。無論您是機器學習新手還是經驗豐富的從業者，本書都提供了廣泛的知識和實用的見解，以提升您的建模技能。