Friday, November 22, 2024
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The 7 Steps of Machine Learning (AI Adventures)

Artis Modus



Google Cloud Platform

How can we tell if a drink is beer or wine? Machine learning, of course! In this episode of Cloud AI Adventures, Yufeng walks through the 7 steps involved in applied machine learning.

The 7 Steps of Machine Learning article: https://goo.gl/XEo6i2

Watch more episodes of AI Adventures here: https://goo.gl/UC5usG

TensorFlow Playground: http://playground.tensorflow.org
Machine Learning Workflow: https://goo.gl/SwLnSz
Hands-on intro level lab Baseline: Data, ML, AI → http://bit.ly/2KoBF6Y

Want more machine learning? Subscribe to the channel: https://goo.gl/S0AS51

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47 thoughts on “The 7 Steps of Machine Learning (AI Adventures)”

  6. @03:01 Why would it be biased to detect beer more often than wine? Would that not mean that it is just better at finding out if something is beer than it is at finding out wether something is wine?

  8. This was a damn good video. Correct me if i'm wrong, but it seems like essentially what your doing is teaching the computer how to use the scientific method. I'm sure that's a very over simplified explanation, but as I was watching this it started to seem very familiar.

  13. google should get there AI to recreate its own algorithm to out perform its existing algorithm to learning and strength it mean while when it's recreating its own AI algorithm's people could teach it what we rely want and need this would give us AI of the 2080s

  14. Nice video, nice explanation of ML. more videos or even a series would be most appreciated. IA and other advanced concept should be taught same way

  19. Very interesting. How would you handle situations where datapoints from two different categories overlap? A white wine that is close in colour and alcohol content to a white ale? Also, the model you describe is a linear split between the categories. But is that always the case?

  27. quick summary:
    – machine learning is all about seeing some examples of input-output pairs and then being able to predict the output for new inputs
    – basically, you feed a bunch of examples to a machine, and the machine will start to learn about the defining characteristics of your examples
    – therefore, it is extremely import that you feed it good examples! Generally, the more examples the better, but you also want your examples to have the distinguishing features in them.
    – once you gather some good examples (with distinguishing features), you generally clean it up, plot it, do some statistical analysis, etc
    – then you choose one of the many different machine learning models (e.g. linear, neural network, etc). Each has its pros/cons. Depending on your examples, and your time constraints, you will pick one of these models
    – you will then tune some parameters of the model (again how you do this depends on your examples and time constraints)

    Hope that was helpful!

    Thanks for the awesome video 🙂

  40. Great pace but the lack of accuracy may lead a newbie to big confusion. 1-The shape of b is not correct, 2-you illustrate linear regression while it is a logistic regression case and 3-we choose model parameters using validation data set before the model evaluation using test data set not after.

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