Machine learning is a process involving the training of a program or model regarding the utilization of data in an approach of interest. The common stages of designing a machine learning paradigm include programming the machine learning model, feeding the necessary data, and training the machine learning model for the analysis of the data.
The world is becoming more and more data-dependent over the past few years. And given the boom in the fields of analytics, machine learning is enjoying a broader field of application. Machine learning tools are designed to serve a very specific purpose or purposes of the same genre. Clearly, different types of training paradigms are involved in preparing the variety of machine learning tools we use today. This article will discuss the different types of machine learning in terms of the type of training and data. And the problems they are deployed for solving.
There are mainly three types of machine learning, supervised, unsupervised, and reinforcement learning.
In the case of supervised learning, the machine learns under the guidance of data. Supervised learning involves labeled input and an explicitly predetermined output. In this type of machine learning, the starting and ending points of a process are predefined.
Types of problems solved
Regression stands for the prediction of a continuous quantity. Labeling, in this case, is redundant, but a predicted output is important for concluding a regression.
Classification problems are identified as segregation of data in classes and labeling the same in classes according to given parameters.
Unsupervised machine learning involves the training of machine learning models with unlabelled data.in this case, a data set is given to an ML tool and the program itself is expected to figure out the inputs of interest. The output is derived by recognition of patterns and trends in the data set, most of the time predictive in nature.
Types of problems solved
Association problems are classified by the requirement of discovering patterns, co-occurrence, frequencies, and similarities between different entities in a given data set.
Clearly, clustering involves placing data from a given database in clusters according to the similarity. Clustering methods are applied for detecting anomalies by recognition of patterns and repetitions in a dataset.
Reinforcement learning is following the middle path. It incorporates components of both supervised and unsupervised learning methods. Reinforcement, in this case, means to promote and establish a pattern of behavior. This kind of learning exposes a model to an unknown data environment and taught to analyze the same using trial and error methods. After the competition of the training, a fresh data set can be presented for analysis.
The cycle of rewards and punishments
The input in the case of reinforcement learning is based on actions made by the agent( ML tools in this case). Hence, the problems solved by reinforcement learning are candid and novel by nature.
An enthusiast willing to enroll in a machine learning course must familiarise themself with the basics of how a model is trained for deployment. For a relatively smooth tenure. The aspects summarized in this article are for providing a lead for further investigations.