Various Types of Machine Learning and Real-Time Applications

Various Types of Machine Learning and Real-Time Applications

Technology is evolving primarily the new-age tech like artificial intelligence and machine learning has gained a lot of significance over the last several years with their applications across the industries. Contrary to what we may expect generally, use cases of machine learning are not so difficult to accomplish. We can see the most evident applications of machine learning in as automated image tagging done by Facebook and the spam detection done by Gmail etc.

As of late, machine learning is capable of resolving many major challenges across industry domains by using various datasets. In this article, we will explore some real-time examples of the most relevant problems solved by machine learning and the ways through which it enables businesses to leverage data accurately.

Advanced machine learning algorithms now are capable of handling the most complicated statistics and derive patterns from huge chunks of data, which encompasses everything from numbers, images, words, and so on. The digitally stored data from various structured and unstructured sources can be digitally stored and fed into machine-learning algorithms for problem-solving and gaining actionable insights.

Various types of machine learning

Further, we will discuss three of the major machine learning types, which can be further trained for everyday utilities.

  1. Supervised machine learning

This is one of the most fundamental types of machine learning. In supervised learning, available data is labeled accordingly that the machine will be able to derive the exact patterns which it should search for. Even though the data has to be accurately labeled for this mode of work, supervised learning will provide better results while used in the right context.

A real-time example of supervised learning is like when we play a Netflix series; machine learning algorithms play in the background to display similar shows based on your search preferences. For this, the ML algorithm is given supervised training with a training dataset which will be a smaller part of the actual bigger database. This will help train the algorithm to get an idea of the problem and its solution to deal with real-time scenarios.

  • Unsupervised Learning

As the name suggests, contrary to supervised learning, there are no data labels available in unsupervised machine models. The machine may look into the patterns randomly, for which human involvement is required to set the dataset for machine-reading. It also lets the program work on much bigger datasets. This mode of machine learning is not as popular as supervised machine learning as it still has not much relevance in day-to-day life applications.

  • Reinforcement machine learning

In this mode of ML, there will be a certain class of problems with which the agents work in a setup with no training datasets. In this mode of machine learning, favorable outputs are encouraged or reinforced along with discouraging unfavorable outputs. This works on an algorithm that will improve by itself over time. This is a trial-and-error model of learning with a clear objective to achieve.

Real-world applications of machine learning

There are various real-time machine learning applications in place now. There are client-centric external applications like customer service, product recommendation, size recommendation, demand forecasts employing machine learning for businesses, etc. there are also internal applications to help the businesses and employees to speed up processes and smart insights to save time and cost. For machine learning algorithms to work, there should be a reliable data store, which can be achieved through the database administration services of Let us discuss a few real-time machine learning applications further.

Spam filtering – You can see that most of the email inboxes now have the capability to segregate the incoming mails to spam, promotional, and social, etc. The email provider uses machine learning algorithms to filter the emails automatically. Providers use trained machine learning models to identify spam emails based on some common characteristics of the same.

Product recommendations – Machine learning-based recommenders are embedded with all the e-com stores and other customer-centered applications we use in daily life. Such recommendations are made based on the behavioral data of the consumers and some common parameters like purchase history, page views, item views, clicks, purchase, form fill-ins, item details, browsing history, etc. This will let such businesses enjoy more traffic and increase conversions.

Segmentation Segmentation of customers with customer lifetime value and churn predictions, are also considered to be the major challenges in customer segmentation. This was one of the biggest challenges for the conventional marketers, which is now largely taken care of by the machine learning mechanisms. Businesses now have a fairly large among of data from various sources, with which it is not possible to do any assessment manually. Using proper data mining strategies and machine learning algorithms, marketers can achieve their incentives by eliminating guesswork and taking actions based on data-driven insights.

Image recognition

There are also further advancements in machine learning in terms of deep learning, which enables image and video recognition, including detection of objects, text detection, image recognition, visual search as provided by Google, video recognition, etc. These are used in various areas, and machines are now good at processing customized images based on the inputs. Deep learning frameworks can read and classify images in a data set more precisely now.

Fraud detection

Business transactions have always faced the threat of fraudulence of many kinds. However, it is impossible to scrutinize each transaction for fraudulence, considering the time and cost involved in ensuring its efficiency. Businesses can easily create data-based queues to investigate the priority incidents and take necessary actions on time. The use of machine learning in finance can also build an accurate predictive model for identifying the fraudulent activities at the first point and giving alerts.

Along with the above few, some other top use cases of machine learning in business include demand forecasting, use of virtual assistant for sales and customer care, sentiment analysis for marketing planning, automation of customer service, etc. Machine learning is constantly evolving, and we can expect the use cases of the same also will expand in the near future. In order to effectively navigate business issues through the solutions, every business needs to keep an eye on the evolving strategies of machine learning and adopt these technologies for their benefit.

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