Develop high-performance and low-bias machine learning and deep learning models and learn new skills to explain how your models work in practice Key Features Learn how to improve performance of your models and eliminate model biases Efficiently design your ML systems to decrease chances of failure in production Apply your knowledge to real-world problems in different areas Book DescriptionDebugging Machine Learning Models with Python is a comprehensive resource that takes you from a basic understanding of ML to an expert level. It goes beyond simple code snippets for model training and testing, providing the knowledge necessary to build reliable models for industrial applications. The book covers essential topics such as designing modular systems for data preparation, accurate model training and testing, and seamless integration into larger technologies for end users. Unlike other resources that focus solely on foundational concepts or delve into advanced topics, this book acts as a bridge between the two. By the end of the book, you will have the skills to evaluate model performance accurately, identify sources of issues, and address them effectively. You will learn techniques to improve your models and avoid data processing and modeling problems right from the start, ensuring high-performance models in production. Additionally, the book explores cutting-edge advancements in deep learning and generative modeling, offering hands-on experience using popular Python libraries such as PyTorch and scikit-learn. Whether you are a beginner or an experienced practitioner, this book will equip you with the necessary expertise to succeed in real-world machine learning applications.What you will learn Use statistical methods to increase data quality and eliminate data flaws Correctly assess and efficiently improve performance of your ML models using Python Explore and improve DL models built in PyTorch Review the best practices to decrease biases and achieve fairness Understand how to use explainability techniques to improve model qualities Learn test-driven modeling to avoid issues in data processing and modeling Explore testing techniques to bring reliable models to production and integrate them into larger technologies Discover the challenges with domain-specific modeling such as in healthcare and finance Who this book is forThe books is for data scientists, data analysts, machine learning engineers, Python developers, and students who want to learn how to build reliable and high-performance industry-level ML models and how to integrate them as part of bigger technologies. Basic proficiency in Python is all you need to understand the concepts and practice problems and code provided in the book. For experienced ML practitioners, this book has a lot to offer in terms of breadth of knowledge in ML modeling.