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.
publication date
2023-09-15
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
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
approach
Discover diverse topics in model debugging and quality improvement through Python code examples, intuitive visualizations, and accessible theoretical explanations. With hands-on experiences, you'll quickly apply the skills and tools learned in this book to your own projects, becoming a proficient and confident expert in machine learning modeling.
audience
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.
short description
Debugging Machine Learning Models with Python equips you with the skills needed to transition from a machine learning beginner to a specialist. It guides you through the tools, theoretical concepts, Python modules, and libraries for bringing a high-quality model into production and identifying opportunities to improve your models.
subtitle
Develop high-performance, low-bias, and explainable machine learning and deep learning models
keywords
Hands-on machine learning; machine learning book; machine learning Python; casual machine learning; reliable machine learning
Product ISBN
9781800208582
