How does machine learning work?

You know now how machine learning works theoretically. But let’s dig deeper and see what the exact structure of it is.

The process of machine learning can be divided into 5 simple steps.

  1. First, the data that is required to begin the process of learning is gathered.
  2. Then it is optimized to achieve the best form of information so that learning can be simplified. It also finds the most critical features and reduces dimensionalities.
  3. The fitting stage is the actual learning state, where the machine’s algorithm analyzes the before formatted data.
  4. After the previous step, it’s time for testing and evaluation.
  5. If there’s a need for it after tests, the algorithm is tuned to raise its performance. That means going back to the training stage – we can only know that the algorithm is improved when we go through the process again and see better results.

As you can see, the process doesn’t seem that elaborate or complicated. But it is based on a sophisticated math foundation that emerged in the 19th century. Ada Lovelace, the pioneer in computer sciences, titled the first computer programmer, stated that every phenomenon in the world could be obtained using a correct math formula. Her theory concluded that machines could understand the world without any help from humans whatsoever. With today’s advancement in science and technology, it became real in the form of machine learning.

Machine learning is a different offshoot of artificial intelligence (AI) and computer science that employs data and algorithms to replicate human thought processes.

Let us understand how machine learning works by breaking it into 3 parts:

  • A Prediction and Classification Process: Machine learning algorithms are used to produce predictions and classifications in general. Your algorithm will find an estimate about a pattern in the data based on some input data, which can be inputted in labeled or unlabeled formed data.

  • An Error Function: it is used to check the model’s accuracy. If there are any known examples concerning which, an error function can compare the model’s accuracy.

  • An optimization process: If the model can better fit the data available in the training set, weights are changed to reduce the difference between each known example and the model estimate. The algorithm will repeat this assessment and optimize the method, updating weights on its own until a particular level of accuracy is reached.