What better way to have fun than scrolling some memes?
Memes have become an imperative part of our everyday lives. As a proof, we now have more memes every day than them Good Morning wishes in family groups.
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Well, why do we need to learn ML? What is ML?
Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions.
Computers learning how to do a task without being explicitly programmed to do so.
Or, in a more friendly definition, Machine Learning Algorithms are those that can tell you something interesting about the data (patterns !), without you having to write any custom code specific to the problem. Instead of writing code explicitly, we feed data to these ML algorithms and they build their own logic based on the data and its patterns.
An example, again, is that you can make an ML model to automatically detect and delete them Good morning wishes posters/images with striking accuracy.
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Irritating, aren’t they ?
And that’s just the tip of the iceberg. There’s a lot more that is done using ML. If you see your daily usage, everything from Google Search prediction, Autocorrect, weather prediction, Google assistant (or Siri or Alexa), facial recognition; requires and implements ML in one way or another.
So I guess you’d know by now what can ML do.
So here’s one on that:
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PS: ML enables the machine to do it all. Paint a canvas, write a symphony et all.
And memes might as well be one good way to get started with ML, and this blog might help.
For those of you who are already “Machine Learning Enthusiasts”, you’d have no difficulty relishing these meticulously made mesmerizing ML memes.
If you’re someone who doesn’t know much about ML, here’s what Andrew Ng’s got to say:
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So the first question, again, What is ML?
We saw the definition already, well, here’s a memer’s take on this:
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MATH + ALGORITHM = MACHINE LEARNING
And here’s what Wikipedia says:
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.
Decide for yourself what you like better.
You’d notice the word statistics in the definition. Well ML did pop up out of Statistics and even today, some of the models used regularly are nothing but statistical calculations.
So, we now present, this:
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And
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Mathematics and ML have had a long long relationship.
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And because of this relationship, many students find it hard to study ML, because, well, Maths.
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I so wanted to share this ML meme. ;)
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true, indeed :(
But then,
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Well this is one of Andrew Ngs favourite dialogue(Andrew Ng: hailed as gawd by people starting ML from his courses (they’re goood goood courses, see this course, and this one too) )
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Those who know, know.
But you don’t need to have had scored an A (or A+ ;) in maths to be able to use ML for your projects or be well versed with these models. And many a people don’t even care about the mathematics behind ML.
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So one more question that arises frequently is :
How are ML and AI different?
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Jokes apart, Artificial Intelligence is defined as any technology which appears to do something smart, or say, mimics Human Behaviour. This can be anything from programmed software to deep learning models which mimic human intelligence.
Whereas Machine learning is a specific kind of artificial intelligence but rather than a rule-based approach, the system learns how to do something from examples rather than being explicitly told what to do.
At this point, you’d be impressed by what ML and AI can do, but there’s a dangerous aspect to it as well. If not used carefully, this tech can be dangerous, but thats not as much of an issue as the media portrays it to be.
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Another term that’s often interchangeably used with ML is Deep Learning
So what is Deep Learning?
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Crying yet?
No, not this 😂.
So Deep learning is a specific type of machine learning using a technique known as a neural network which connects multiple models together to solve even more complex types of problems. (more on Neural Network later)
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