Machine Learning Methods

Here’s an overview of the most widely adopted machine learning methods. Few of them are,

Supervised learning 

These algorithms have use of  labeled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. Then  it modifies the model accordingly. Methods like classification, regression, prediction and gradient boosting, supervised learning use their patterns to predict the values of the label on unlabeled data.

Applications: Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.

Unsupervised learning 

It is used against data that has no historical labels and the system  don’t say the right answer. The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition  are also used to segment text topics, recommend items and identify data outliers.

Applications: It can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other.