Hello World – Machine Learning Recipes #1

Six lines of Python is all it takes to write your first machine learning program! In this episode, we’ll briefly introduce what machine learning is and why it’s important. Then, we’ll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up.

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Comment (35)

  1. Hey everyone, I'm trying to remodel the code seen in 5:42 in pycharm. I successfully added tensorflow and sklearn as packages of my interpreter. But every time when I'm writing the first line of code, it turns gray. What am I doing wrong? Has someone any ideas?

  2. Sir your videos on Machine Learning Recipes are really amazing and easy to understand but can you please reference some textbooks that are easy to understand for beginner and would cover topics in detail

  3. So machine learning is package and dependency hell.
    Aka good luck with GPO and UAC on Windows 10.
    Thinking of picking up a chromebook or some small laptop and get a unix/gnu distro if ima get into this .
    I see what you did there, Google ?
    I bet thorough tutorials using cmd or PowerShell on Windows 10 are scarce. Like usual. Thanks #Microsoft #Win10Probs

    Btw not trying to hate im just joking mostly.
    Thank you for your articulate tutorial nonetheless

  4. I am working with certain sensors and i am not getting stable readings. Lots of jitter for even a stable position. Like the value keeps on shifting between 2-3 values. Is it possible to use machine learning for obtaining a stable reading? Like is there some classifier I can use?

  5. After watching a lot of other AI guys, was thinking this guy sounds very professional wow he did really well for his (personal) channel … lo and behold it's Google! 🙂

  6. Wonderful. Here is my attempt at the Sportscar-Minivan example!

    from sklearn import tree
    #features– horsepower,seats (supervised learning)
    features=[[300,2],[450,2],[200,8],[150,9]]
    #labels 0-sportscar, 1-minivan
    labels=[0,0,1,1]
    clf=tree.DecisionTreeClassifier()
    clf=clf.fit(features,labels)
    res=clf.predict([[250,5]])
    if(res==0):
    print("It is a Sportscar")
    else:
    print("It is a Minivan")

  7. "ValueError: Expected 2D array, got 1D array instead:
    array=[160. 0.].
    Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
    Data neither has a single feature nor contains a single simple. Any ideas?

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