Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial

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Published 2022-06-15
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: youtube.com/ycubed
🐦 Twitter: twitter.com/kylieyying
📷 Instagram: instagram.com/kylieyying/

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

⭐️ Resources ⭐️
💻 Datasets: drive.google.com/drive/folders/1YnxDqNIqM2Xr1Dlgv5…
💻 Feedforward NN colab notebook: colab.research.google.com/drive/1UxmeNX_MaIO0ni26c…
💻 Wine review colab notebook: colab.research.google.com/drive/1yO7EgCYSN3KW8hzDT…

⭐️ 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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All Comments (21)
  • @KylieYYing
    Thanks for watching everyone! I hope you enjoy learning from the examples in this course :)
  • @mohitgangrade351
    This is exactly what I was searching yesterday! You're amazing! Thanks for this tutorial. :)
  • @Luisa_Ribeiro
    That was so well-explained and practical! Looking forward to more of these on other types of machine learning models! Thank you!
  • @jyotichetry08
    you way of explaining is so good this was the first video i watched on Neural networks and iam already in love with it.
  • @francis.joseph
    great content. explained in layman terms without wasting time 👌🏻
  • @yizzi25
    Really great video, great explanation of concepts in very easy/ layman terms. Well done!
  • @prajwaldeepkhokhar7416
    20 minutes in and am all in. I teach students ML and Data Science, and i keep studying the same myself. The young lady in the video covered all the necessary basics, and did it so well i might end up suggesting the same video to my students on multiple occasions. And yeah, at the end of this video, i am going to her channel and subscribing. Keep up the good work <3
  • @mercykiria5880
    finally!! i have finally understood everything after a month of struggling to do so. thank you sooo much
  • @RolandGrafe
    I find your tutorial very interesting, very clear, and very convincing. My question: Also, is there a tutorial that shows the practical application of the model you created? - I would like to learn more about how this model can be practically used for evaluating and analysing new data.
  • @ashuu9257
    a reinforcement learning course please,please , please , really need it & you're so amazing at simplfying things and making them understand
  • @michelletan4249
    You are so awesome! this is I am searching for! it is really help a lot! Thank you all you hard work and precious time!
  • @foremarke
    Thanks so much Kylie, good coding tutorial and excellent, sharp run through ML theory! Thanks again.
  • @stories_VX
    ⭐ 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
  • @xunililak1674
    Nice video, you really sparked interest in ML and are looking foward to future content! Keep it going!
  • @suomynona7261
    Thank you for making this! Please make it a series if you can
  • @abtiwary
    Thank you so much for your brilliant tutorials and courses Kylie (please do more!!!)! Could you please recommend some books on the mathematics of machine learning (and books that you found useful when you dived into the subject).
  • @User_unknown1838
    @21:04 when kylie was explaining multiclass and binary classification with the example of hotdog, I first remembered Jian yang's app from Silicon Valley. I really liked that you put in a small clip of it.