- Course
Model Training: Best Practices for Data Practitioners
This course will teach you how to apply best practices for training machine learning models, from preparing quality data and choosing the right algorithms to splitting datasets and monitoring models for long-term performance.
Beginner
- Course
Model Training: Best Practices for Data Practitioners
This course will teach you how to apply best practices for training machine learning models, from preparing quality data and choosing the right algorithms to splitting datasets and monitoring models for long-term performance.
Beginner
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This course is included in the libraries shown below:
- AI
What you'll learn
Training great machine learning models requires best practices at every stage of the process.
In this course, Model Training: Best Practices for Data Practitioners, you'll learn how to build and train models in a way that leads to reliable, ethical, and production-ready outcomes.
First, you'll explore the full machine learning lifecycle, and see how each stage impacts training success.
Next, you'll discover how to prepare high-quality data through cleaning, preprocessing, normalization, and standardization.
Finally, you'll learn how to select the right algorithms, perform train-test splits, and adopt continuous learning strategies to keep models accurate as data changes.
When you're finished with this course, you'll have the skills of model training needed to develop machine learning models that deliver consistent business value.
Model Training: Best Practices for Data Practitioners
Beginner
Table of contents
Daryle has developed products for Lockheed Martin, Tesla, and other companies across various industries. He also teaches data science and software engineering to young aspiring professionals.