Digitization of the world is happening at the exponential rates. As per IDC, a Market Intelligence Company, the world’s data will grow to 175 Zettabytes (1 zettabyte = A trillion Gigabytes) in 2025. We are creating lots of data in each and every field.

But, are we capable of handling and processing this data and draw a meaningful conclusion out of it?

As humans we have limitations of processing the data, that’s where machine learning comes to our rescue.

Machine Learning (ML) is a category of algorithms that allows software applications to become more precise in predicting outcomes without being explicitly programmed.

Its basic premise is to build algorithms which receive input data and use statistical analysis to predict an outcome while updating outputs as new data becomes available. Machine Learning is touching our lives on a day to day basis.

Surge pricing strategy used by Uber uses machine learning to revise the price in real time.

With Machine Learning, systems can now learn from data, identify the patterns and make decisions with minimal human intervention.
Isn’t that great?
Just imagine how far can it take the human race. Artificial Intelligence, the next big revolution, completely depends on how can we make the machines learn certain things. So, Machine Learning is crucial for taking better business decisions through data and live a better quality of life.

How machine learning works

There are two categories of Machine Learning Algorithms, Supervised and Unsupervised.


Supervised algorithms need a data analyst or data scientist with machine learning skills to provide both input and desired output. Data scientists determine which all variables or features, the model should analyze and use to develop predictions.


These algorithms don’t require to be trained with desired outcome data. They use an iterative approach (deep learning) to review data and arrive at conclusions. Unsupervised machine learning algorithms are used for more complex processing tasks than supervised machine learning algorithms.


How Machine Learning is helping the ecosystems


Product Recommendations

Many E-Commerce companies are using Machine Learning for making product recommendations. ML algorithms use customers’ purchase history and match it against product inventory to identify hidden patterns and group similar products together. Group of products is then suggested to customers which increase the probability of purchase.

Medical Diagnosis

ML helps many healthcare organizations to do almost perfect diagnosis, predict readmissions, recommend medicines and identify high-risk patients. For example, Stanford developed a machine learning algorithm which is capable of diagnosing skin cancer by learning from the data and imaging fed into it.

Financial Analysis

Financial Organizations have large amount of quantitative and historical data. ML is extensively being used for processing this data and do portfolio management, algorithmic trading, fraud detection, and loan underwriting. Future applications of ML in finance include conversational interfaces for security, customers service, and sentiment analysis.

Improving Cyber Security

Machine Learning is allowing providers to build new-generation technologies which efficiently and quickly detect unknown threats.

Predictive Maintenance

Manufacturing firms follow preventive and corrective maintenance practices which are expensive and inefficient. ML helps to identify patterns hidden in the factory data and discover meaningful insights. This predictive maintenance helps in reducing the risks associated with unexpected failures and remove unnecessary expenses.

Customer Lifetime Value Prediction

Companies have a huge amount of customer data which can be used to make better business decisions which ultimately can increase their revenue.
Customer Segmentation and Customer Lifetime Value Prediction are crucial matrices for marketers.
Machine Learning helps businesses predict customer behaviors & purchasing patterns and it also helps in sending the best possible offers to individual customers based on their browsing history and purchasing patterns.

Increasing Customer Satisfaction

Machine Learning can be used for superior customer experience. Client requirement can be correctly assigned to the most suitable customer service executive based on previous call records for analyzing the customer behaviour.
This way, Machine Learning drastically reduces the associated costs and the amount of time invested in managing the customer relationship.

Eastern Enterprise Leveraging Machine Learning

SmartBooqing is the world’s smartest invoice processor. It’s a product developed by Eastern Enterprise leveraging Machine Learning algorithms.
It’s capable of fully automating the accounting systems.
Machine Learning Algorithm developed by SmartBooqing makes it the only invoice processor that can process every invoice, both paper and digital (UBL) at line level.
This way, you can process invoices faster, with high quality and great consistency.

Get back to us if you have any queries regarding Machine Learning or if you want to implement it for your organization.