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Stay ahead of the Machine Learning curve

Manpreet Kalsi
min read
Stay ahead of the Machine Learning curve

So in the past few years, artificial intelligence has evolved to a point where it changed the whole world in a million ways. As a matter of fact, artificial intelligence has become a crucial part of our lives today and we use it in various ways, whether we know it or not. All AI applications include learning, reasoning and self-correction. AI is growing today and adapting expert systems, speech recognition and machine vision. One such application of AI is Machine learning. In other words, you can also think of it as a subset of AI.

In this blog, we’ll see what machine learning is all about. How is it growing big globally? How are businesses stepping up their game because of machine learning?

Let’s start with, what is Machine learning?  

So basically machine learning is an application of AI that provides the system the ability to automatically learn and improve based on its own experiences, without having anyone to program them. Machine learning’s local point is the development of computer programs so that they can access data and learn it for themselves. The process of learning happens with observations or data, either in the form of instructions, experience or examples to form the learning pattern, because the primary aim is to allow the computers to learn automatically without human assistance.

Machine learning extracts meaningful insights from raw data to solve complex problems quickly. Certainly, ML is growing at a rapid rate and has gained tremendous popularity within the business analytics community. To name a few of the main components that resulted in the wide growth of machine learning are- easy availability of data, growing volumes, faster and cheaper computational processing. So let’s see how businesses are utilising and making profits out of machine learning.

How machine learning helps in businesses?

Eliminate manual data entry:

One of the most frequently occurring problems that businesses face are incorrect data entries. Machine learning gathers data by itself and significantly avoids any errors. Therefore, the employees can focus on tasks that add value to the business.

Product recommendations:

Product-based recommendations systems are going big in the e-commerce websites. ML algorithms use customer's purchase history to make patterns and group similar products together. These products are then suggested to the customers, therefore pushing product purchase.

Image recognition:

Image recognition can produce numeric and symbolic information from images , meanwhile also known as computer vision. It involves data mining, pattern recognition, data based knowledge discovery and much more. Mainly healthcare and automobile industries make use of this.

Medical diagnosis:

This has been taking the healthcare industry by storm. This ML application makes perfect diagnosis, predicts readmissions, recommends medicines and identifies high-risk patients. These predictions are drawn using patient records, data sets and symptoms.

Customer value predictions:

So how can ML can help businesses personalise their customer experience? Companies have huge amounts of data and ML can effectively use them to derive meaningful business insights. How ML helps in personalising customer experience is- they gather purchasing patterns, predict customer behaviour and send best possible offers to the customers based on browsing and purchase history.

Increasing customer satisfaction:

ML has been helping businesses step up their customer experience and improve customer loyalty. ML analyses customer behaviour based on previous call recordings and in accordance with the client requirements, the most suitable customer service executive is assigned. This reduces the cost and time that businesses usually put into managing customer relationships. Major organisations use predictive algorithms to provide their customers with suggestions of products they enjoy.

Accurate analysis:

So we all know that businesses use large amounts of qualitative and quantitative data. Using this, ML can pull out financial analysis. ML can be used in portfolio management, loan underwriting and fraud detection. Its further applications include sentiment analysis, chatbots and conversational interface.

Increasing cyber security:

Cyber security is one of the major problems solved by Machine Learning. Because ML allows newer technologies to be built, it can easily detect any unknown threats.

Wrapping up!

Machine learning has not only helped businesses boost their overall workings but also improved their structure. As ML grows rapidly by each day, newer technologies are taking over and automating the way businesses work. With this said, we introduce to you ML related services- chatbots.We’re a robust chatbot building platform which offers quick and easy chatbot building.

Check Engati and register for free or schedule a chatbot demo.

We hope you found this blog helpful. Thanks for reading!

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