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.

?? Resources ??
? Datasets: https://drive.google.com/drive/folders/1YnxDqNIqM2Xr1Dlgv5pYsE6dYJ9MGxcM?usp=sharing
? Feedforward NN colab notebook: https://colab.research.google.com/drive/1UxmeNX_MaIO0ni26cg9H6mtJcRFafWiR?usp=sharing
? Wine review colab notebook: https://colab.research.google.com/drive/1yO7EgCYSN3KW8hzDTz809nzNmacjBBXX?usp=sharing

?? 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)

? Thanks to our Champion and Sponsor supporters:
? Raymond Odero
? Agustín Kussrow
? aldo ferretti
? Otis Morgan
? DeezMaster

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