Debugging Machine Learning Models with Python
Develop high-performance, low-bias, and explainable machine learning and deep learning models
Created by Ali Madani
Gain practical skills to debug and improve your machine learning models using Python. Explore tools and techniques for building reliable, high-performance, and explainable models ready for production. Move beyond theory and start solving real-world challenges with confidence.
Packt | Sep 2023 | 344 min
What You Will Learn
You will work through hands-on Python code examples and visualizations to spot and resolve common modeling problems. By applying practical debugging strategies and exploring real scenarios, you'll learn to evaluate, optimize, and explain your models for better results in any industry.
Key Features
- Identify and fix model issues to boost performance and reduce bias
- Apply explainability techniques for more transparent and trustworthy models
- Integrate models into production systems using proven Python libraries
Target Audience
Ideal for data scientists, analysts, machine learning engineers, and Python developers with basic Python skills. If you want to build robust, fair, and explainable models for production, or deepen your expertise in debugging and optimizing machine learning systems, you'll find actionable guidance and practical insights here.
