Data-Centric Machine Learning with Python
The ultimate guide to engineering and deploying high-quality models based on good data
Created by Jonas Christensen, Nakul Bajaj, Manmohan Gosada
Explore the essentials of data-centric machine learning and discover how focusing on data quality can transform your AI projects. Learn practical techniques for collecting, labeling, and refining data with Python, and see how these methods can help you build more reliable and accurate models.
Packt | Feb 2024 | 378 min
What You Will Learn
You will start by understanding the core principles of data-centric machine learning and why data quality matters. Through hands-on Python examples, you will practice data collection, labeling, cleaning, and augmentation. Real-world scenarios will help you apply these techniques to build trustworthy machine learning solutions.
Key Features
- Master techniques for collecting, labeling, and refining data using Python tools
- Apply best practices for generating and working with synthetic data in real projects
- Develop skills to detect bias and ensure ethical, reliable machine learning outcomes
Target Audience
This content is ideal for data scientists, machine learning practitioners, and technical leaders who already have some experience with Python and want to improve their data-centric skills. If you are aiming to build more robust models by focusing on data quality and ethical practices, you will benefit from these practical approaches.
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