A Guide for Non-Tech Co-Founders on Machine Learning in Simple Terms!

The newest buzzword in use is machine learning. Given that it is among the most fascinating areas of computer science, it deserves to. So what exactly does 'machine learning' mean? Machine learning, by definition, gives computers the capacity to learn without being explicitly programmed.

You realize that you used too much force on the first try. After the second attempt, you see you are getting closer to the goal but that your throw angle needs to be raised. Basically, what is occurring is that we are improving the outcome by learning something after each toss. We are wired to draw lessons from our past.

The same thing is possible with machines. A machine can be programmed to learn from all tries, experiences, and data points and then enhance the result. We'll look at a paper tossing example using both machine and non-machine methods.

Unspecific Program (Non Machine Learning)

In the case of the aforementioned example, a general software would instruct the computer to assess the distance and angle and use a pre-established formula to determine the necessary force. Adding a fan (wind force) to your setup will cause this software to consistently miss its aim and prevent it from learning from its mistakes. You must rewrite your calculation to include the wind factor in order to get the desired result.

A programme for machine learning

Now, using the same example, a machine learning programme would start with a generic formula but refactor it after each attempt or experience. The result also increased as the algorithm was regularly updated utilizing more experiences (data points). The YouTube Video Recommendations allow you to observe these things in action all around you.

A computer programme is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This is a definition of machine learning that is more technical. This essentially means that with experience, a computer learns to perform any task better. In our paper toss scenario, we precisely saw this happen.

The age of a person can be determined from his date of birth without the aid of a machine learning system. However, you may infer a person's age from his musical preferences using a machine learning system.