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Key benefits

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

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.

Who is this book 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.

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

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Colour book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Causal Inference and Discovery in Python

€29.99

Interpretable Machine Learning with Python

€37.99

Debugging Machine Learning Models with Python

€29.99 ~~€37.99~~

Total € 97.97 105.97 8.00 saved

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Shipments are not trackable

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Amazon verified reviews

Amazon Customer Mar 25, 2024

What I like about it the most is that even though it’s comprehensive, accurate and detailed, it’s easy tofollow and a pleasure to read.I’d recommend this to data scientists, machine learning engineers, developers, and students eager to refine their skills in crafting production-ready, ethical, and explainable ML models.

Amazon Verified review

Mehrdad Mastali Feb 28, 2024

This book is an essential guide for data scientists on the machine learning lifecycle, offering practical insights for developing reliable and high-performance models. Its clear explanations, real-world examples, and focus on the best practices make it invaluable for both beginners and experienced professionals seeking to enhance their ML projects.

Pooya Mirzabeygi Oct 12, 2023

This book is what I've been looking for so long! I've read many machine learning books and often got caught up in some complex math details that discouraged me from moving forward. This book however, really starts from the basic fundamentals and takes you to advanced concepts with real-world examples and simple language in a clear path. It also helped me develop a better understanding of machine learning life cycle, model performance and techniques and all you really need to start is some basic understanding of Python. I recommend it to everyone from the beginners to professionals in the field.

H2N Oct 26, 2023

This book is a goldmine for data enthusiasts, ranging from analysts to Python aficionados. Diving deep into machine learning, it marries foundational knowledge with advanced topics, from code debugging and ML life cycles to deep learning and human-centric approaches. With an emphasis on practical examples, readers are guided through vital areas like model performance, fairness, and security. Catering to both beginners and experts in the ML realm, this book seamlessly blends basic insights with intricate techniques, making it a must-read for all.

pari Feb 10, 2024

Great resource for anyone looking to enhance their understanding of debugging in the context of machine learning.Clear explanations and practical examples. Highly recommended!

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

What do you get with Print?