Code: USD75OFF
This is the code repository for Debugging Machine Learning Models with Python, published by Packt.
Develop high-performance, low-bias, and explainable machine learning and deep learning models
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
import pandas as pd
orig_df = pd.DataFrame({
'age': [45, 43, 54, 56, 54, 52, 41],
'gender': ['M', 'F', 'F', 'M', 'M', 'F', 'M'],
'group': ['H1', 'H1', 'H2', 'H3', 'H2', 'H1', 'H3'],
'target': [0, 0, 1, 0, 1, 1, 0]})
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-17).
| Chapter | Software required | OS required |
|---|---|---|
| 1-17 | Python >=3.6 | Windows, Mac OS X, and Linux (Any) |
| 1-17 | DVC >= 1.10.0 |
Ali Madani worked as the director of machine learning at Cyclica Inc leading AI technology development front of Cyclica for drug discovery before acquisition of Cyclica by Recursion Pharmaceuticals. Ali completed his PhD focusing on machine learning modeling in cancer setting and attained a master of mathematics from the University of Waterloo. He has also published more than twenty scientific articles in high impact factor journals on applications of machine learning and statistical modeling in healthcare. As a believer in industry-oriented education and pro-democratization of knowledge, Ali has actively educated students and professionals through international workshops and courses on basic and advanced high-quality machine learning modeling.