Why Machine Learning Isn’t As Hard To Learn As You Think

Why Machine Learning Isn’t As Hard To Learn As You Think

Why is Machine Learning difficult to understand? originally appeared on Quorathe place to gain and share knowledge, empowering people to learn from others and better understand the world.

Answer by John L. Miller, Industry ML experience with video, sensor data, images. PhD. Microsoft, Google, on Quora:

I’m usually the first person to say something is hard, but I’m not going to here. Learning how to use machine learning isn’t any harder than learning any other set of libraries for a programmer.

The key is to focus on using it, not designing the algorithm. Look at it this way: if you need to sort data, you don’t invent a sort algorithm, you pick an appropriate algorithm and use it right.

It’s the same thing with machine learning. You don’t need to learn how the guts of the machine learning algorithm works. You need to learn what the main choices are (e.g. neural nets, random decision forests…), how to feed them data, and how to use the data produced.

There is a bit of an art to it: deciding when you can and can’t use machine learning, and figuring out the right data to feed into it. For example, if you want to know whether a movie shows someone running, you might want to send both individual frames, and sets of frame deltas a certain number of seconds apart.

If you’re a programmer and it’s incredibly hard to learn ML, you’re probably trying to learn the wrong things about it.

This question originally appeared on Quora – the place to gain and share knowledge, empowering people to learn from others and better understand the world.

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