Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial

By freeCodeCamp.org
Published: Jun 15, 2022

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This course will give you an introduction to machine learning concepts and neural network implementation using Python and TensorFlow. Kylie Ying explains basic concepts, such as classification, regression, training/validation/test datasets, loss functions, neural networks, and model training. She then demonstrates how to implement a feedforward neural network to predict whether someone has diabetes, as well as two different neural net architectures to classify wine reviews.

Course created by Kylie Ying.
YouTube: https://youtube.com/ycubed
Twitter: https://twitter.com/kylieyying
Instagram: https://instagram.com/kylieyying/

This course was made possible by a grant from Google’s TensorFlow team.

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Resources
• Datasets: https://drive.google.com/drive/folder…
• Feedforward NN colab notebook: https://colab.research.google.com/dri…
• Wine review colab notebook: https://colab.research.google.com/dri…

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Course Contents
• (0:00:00) Introduction
• (0:00:34) Colab intro (importing wine dataset)
• (0:07:48) What is machine learning?
• (0:14:00) Features (inputs)
• (0:20:22) Outputs (predictions)
• (0:25:05) Anatomy of a dataset
• (0:30:22) Assessing performance
• (0:35:01) Neural nets
• (0:48:50) Tensorflow
• (0:50:45) Colab (feedforward network using diabetes dataset)
• (1:21:15) Recurrent neural networks
• (1:26:20) Colab (text classification networks using wine dataset)

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