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4.2: Gathering, refining, and using data effectively for ML model datasets

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Learn the essential concepts around gathering good quality training data that is clean (free of mistakes or errors) and unbiased to ensure the best results possible from the resulting custom model you wish to make. You will also learn the importance of splitting your data into training, validation, and testing datasets to avoid overfitting and ensuring the best ML model creation with the data you have.

Catch more episodes from Machine Learning for Web Developers (Web ML) → https://goo.gle/learn-WebML
Check out TensorFlow on YouTube → https://goo.gle/TensorFlow-YouTube
Subscribe to Google Developers → https://goo.gle/developers

Connect with Jason Mayes to ask questions:
LinkedIn → https://goo.gle/3GwgeLw
Twitter →https://goo.gle/3Xh6MT7
Discord →https://goo.gle/3WWVO5t

Use #WebML to share your learnings and creations from this course to meet your peers on social media!

See what others have already made with Web ML → http://goo.gle/made-with-tfjs
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