Machine learning is an area of computer science that allows computers to learn without being explicitly told what to do. In other words, machine learning allows computers to “learn” by examining data and detecting patterns. This process is called “training” the ML algorithm.
The process of building and defining what you want out of your ML application starts with inputting the proper data into the algorithm. There two sets of training data to feed your algorithm, known or unknown data. Correctly entering this data is crucial for the end result, if done incorrectly you will spend more time “unlearning” your application or having to restart from scratch.
ML algorithms are built in a variety of ways, but they all share the same structure:
Processing is the first task when creating an ML algorithm. This includes removing noise and outliers, changing the data into a proper format, and partitioning it into collections to cleanse and prep the data for training.
Once processing has been completed, next step is to train the algorithm to use the prepped data. During this task, the algorithm will detect new patterns to determine desired outcomes.
The final step in the base structure is prediction. As you might have guessed after the processing and training of the data, the algorithm will now create predictions. This can be performed real time or when the app is dormant.
There are many applications for machine learning to be used in, including picture recognition, real communication processing, and predictive analytics. In addition, machine learning can be used for responsibilities such as fraud detection, speech recognition, and stock market prediction.
Most machine learning algorithms are “supervised” algorithms. Meaning they need a training data that have both input options and target values. Or you can go with an unsupervised algorithm, which do not need training.