Published on October 4, 2018 by

Welcome back to Coding TensorFlow! Predictions from machine learning usually come from either classification problems, or regression problems. In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where we aim to predict a discrete label (for example, where a picture contains an apple or an orange). In this episode, we’ll show you how regression problems work by predicting house values. Watch to follow along, and subscribe to the channel for more Coding TensorFlow episodes!

Step by step doc on predicting house prices: regression → http://bit.ly/2xV8rVg

Watch our Text Classification tutorials → http://bit.ly/2zoZfvt
Subscribe to the TensorFlow YouTube channel → http://bit.ly/TensorFlow1

How are you using TensorFlow in your ML models? Let us know in the comments below!

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