Introduction to ML

Data collection, analysis, and the mining of data to extract information are major agendas of many data-related systems. Processing, analyzing, and executing mining-related functions requires processing time, evaluation, and interpretation to reach the desired state. Using ML, systems can be trained on relevant or sample data and ML can be further used to evaluate and interpret other data or datasets for the final output.

ML-based processing is implemented similarly to and can be compared to data mining and predictive modeling, for example, classifying emails in an inbox as spam and not spam. Spam detection is a kind of decision-making to classify emails according to their content. A system or spam-detecting algorithm is trained on inputs or datasets and can distinguish emails as spam or not.

ML predictions and decision-making models are dependent on data. ML models can be built on top of, and also use, several algorithms, which allows the system to provide...