Machine Learning in Layman’s Terms — A Guide for Non-Tech Co-Founders!

Machine Learning is a latest buzzword floating around. It deserves to, as it is one of the most interesting subfield of Computer Science. So what does Machine Learning really mean? By definition, Machine learning provides computers with the ability to learn without being explicitly programmed.

Let’s try to understand Machine Learning in layman terms. Consider you are trying to toss a paper to a dustbin.
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After first attempt you realise that you have put too much force in it. After second attempt you realise you are closer to target but you need to increase your throw angle. What is happening here is basically after every throw we are learning something and improving the end result. We are programmed to learn from our experience.

We can do something similar with machines too. We can program a machine to learn from every attempts/experiences/data-points and then improve the outcome. Let’s see paper toss example in Machine and Non-Machine approach.

A Generic Program (Non Machine Learning)

In our above example, a generic program would tell computer to measure the distance and angle and apply some pre-defined formula to calculate the force required. Now if you add a fan (wind force) to your setup, this program will continuously miss target and won’t learn anything from it’s failed attempt. To get the outcome right, you need to reprogram taking wind factor into your formula.

A Machine Learning Program

Now, for the same example a Machine Learning program would begin with a generic formula but after every attempt/experience refactor it’s formula. As the formula is continuously improved using more experiences (data points) the outcome too improved. You see these things into action around you in YouTube’s Video Recommendations and Facebook’s News Feed Content etc

Another more technical definition of Machine Learning is —A computer program is said to learn from experience E with respect to some class of tasks and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This basically means in machine learning for any task a machine improves it’s performance with its experience. This is exactly what we observed in our paper toss example.

You don’t need a Machine Learning algorithm to calculate a person’s age from his date of birth. But you would use a Machine Learning algorithm to guess a person’s age using his Music likes. For example your data would point that Led Zeppelin and The Doors fans are mostly 40+ and Selena Gomez fans are generally younger than 25. Machine Learning can be used in literally everything around you. But it’s important to understand that does the problem really needs to be solved through Machine Learning or not.

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