Machine Learning, or ML as it is commonly referred to, allows a computer to learn and carry out interpretation of data without being programmed to do so. The computers when come in contact with new set of data will independently interpret and adapt to the new data from previous computations and patterns.

This is usually achieved by several Machine Learning (ML) algorithms that help in analyzing data. Thanks to advanced computing technologies that has made it possible for this technology to evolve.

The main area that has gained momentum is the fact that machines (computers) have the ability to identify patterns from previous data without being programmed. In other words, machines are left to solve their own work.

How does machine learning work?

It is very important to build the right set of machine learning tools and match them with the accurate algorithm so that you can get your task completed in an optimum manner. Generally speaking, most of the experts make use of Machine learning algorithms that include Random forests, Decision trees, Neural networks, supporting vector machines, mapping nearest neighbor, boosting and bagging gradient, SEO, Multivariate adaptive regression, and last but not the least, analyzing principal components.

Most importantly, it is not just about selecting the right algorithms in machine learning but also making them work in tandem with the right set of tools, which essentially include exploration of data that is followed by assessing the results of data visualization, management, and data quality. Graphical user interface are particularly useful and complements machine learning procedures. Aside from this, the most crucial is the so called data-to-decision processing, which is automated.

Applications of Machine learning

As far as machine learning applications are concerned, most prominent these days is the so-called embedded machine learning applications. The fact that businesses are able to get in-depth insights into data obtained, it is easier for the companies to work thereby effectively controlling costs and to enjoy an edge over the competitors.

Given below are the various sectors where the application of machine learning is prevalent. These are as follows-

Financial services

One of the most important applications of machine learning is this sector is prevention of financial fraud and identifying the opportunities for financial investments and trade. With the help of cyber surveillance techniques, you are also in a better position to identify the clients that are risk prone and how to prevent these clients from falling prey to financial fraud.

Aside from the above, the other sectors that have applied the technology include:

  • Government
  • Marketing and Sales
  • Healthcare
  • Oil and Gas
  • Transportation

Popular methods of machine learning

One of the most widely used applications is detecting malware in Android devices. A lot has been written on this and extensive research carried out in this field. Malware detection with ML is not just restricted to Android devices like smartphones.

Few of the sought after ML learning methods include the following:

Supervised Learning

These are the trained algorithms. In this a set of instructions are received which are compared with the outcome. If an error is encountered the same will appear as flagged, particularly helpful in detecting plastic card fraud.

Unsupervised Learning

As mentioned above, the machine will not be taught to identify any pattern but will have to identify from seeing historical or previous patterns.

Aside from the above, there are 2 other ML methods that are widely used these days and they include Reinforcement Learning and Semi Supervised Learning.

Aim of Machine Learning

The main aim of ML is to assess the hidden pattern in the data and the structure of the same. However, the success largely depends on the computers’ ability to delve deeper into the hidden data and analyze the same.

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