How Crucial is Data Processing for Machine Learning (ML) & Artificial Intelligence (AI)?

By : Satyam Malviya - 20-04-2022

What is Machine learning?

Machine learning is a branch of artificial intelligence. It teaches systems to think like humans. So, any task that can be completed with a data-defined pattern can be automated with machine learning.

Why does Machine Learning Matter?

It is a thriving technology that benefits every type of business across every industry. From healthcare to finance, transportation to cyber safety, & marketing to government, machine learning can support every type of business to adapt and move forward quite swiftly. One doesn’t have to depend on guesswork as algorithms can examine much larger datasets and comprehend connective patterns even quicker than a human, or any spreadsheet ever can. Machine learning empowers businesses to gather insights quickly and efficiently, enhancing the business value. This is why machine learning is important for every business. Plus there is a better chance of pinpointing promising opportunities – or avoiding unknown risks.

Introduction to Data in Machine Learning

Data is the most essential part of data analytics, machine learning, and artificial intelligence. It can be any unprocessed fact, value, text, sound, or image that is not being interpreted and analysed. Data comes in many forms, but machine learning models depend on four primary data types. These include,
• Numerical data
• Categorical data
• Time-series data
• Text data

Understanding Data Processing

Data processing services transforms the data from the given format into a much more useful format, making it more meaningful and informative. The procedure is automated through machine learning algorithms, mathematical modelling, and statistical knowledge. The output of this can be in the form of graphs, videos, charts, tables, images, and many more, depending on the task we are conducting and the necessities of the machine. Data processing in machine learning might seem simpler for smaller firms. But, when it comes to organizations such as Twitter, Facebook, or administrative offices like Parliament, UNESCO, and health sector institutions, the process must be conducted in a remarkably structured manner.Raw Data - Data Processing - Significant Information

The steps to perform Data Processing Services are as follows:

Data collection:

The most crucial step is to have the data of top quality and accuracy. Such data can be gathered from certified & reliable sources. Such data will make the learning process of the model easier and better. While testing the AI model will fetch the best results. Organizations or researchers have to determine the kind of data they need to execute their research and other tasks. For example- to make a facial expression recognizer work efficiently, one has to gather multiple images having a variety of human expressions. Sound data ensures that the results of the model are valid and reliable.

Preparation:

The accumulated data will be raw, having no proper formats. Such data cannot be fed directly to the system. In the preparation stage, datasets are collected from different sources and analysed. Then a dataset gets constructed for further processing and exploration. The preparation can be performed two ways- Manually or through an automatic approach.

Input:

Prepared data is not always in a machine-readable format. To convert the data into a format that can be read easily by the system, conversion algorithms are required. To do the task more effectively, high computation and precision are needed.

Data Processing:

This is the stage where algorithms and ML techniques perform the instructions (provided over a large data volume) with maximum accuracy and proper analysis.

Output:

At this stage, outcomes are acquired by the machine in a way that can be assumed easily by the user. Output can be in the form of reports, spreadsheets, graphs, or videos.

Storage:

In this last step, the acquired output, data model, and all the valuable information are saved for future usage.

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Here’s how machine learning has become a part of our day-to-day lives:

Virtual personal assistants

Siri, Alexa, and Google Now are some of the famous examples of virtual personal assistants. As the name implies, they help in finding information, when asked. They search the information, recall the related queries, or send a command to other resources (like phone apps) to gather data. The virtual assistants also respond and follow the commands such as –‘set an alarm for 6 AM tomorrow’ OR ‘remind me to visit the bank day after tomorrow’.

Video surveillance

These days, video surveillance is powered by AI which makes it possible to detect crimes before they occur. Using the data processing in AI, cameras can track unusual behaviour of people & can alert the authorities who can eventually help to avoid mishaps.

Email spam and malware filtering

There are many spam filtering techniques that email clients use. To confirm that these spam filters are always updated, they are powered by machine learning.

Online fraud detection

One can’t deny that machine learning has made cyberspace a much more secure place. It can efficiently track financial frauds online. Online payment service provider PayPal uses a set of tools that help them to compare countless transactions and differentiate between legitimate or unlawful transactions taking place between the buyers and sellers.

Product recommendations

Say you purchased a product from an online website or application some days back, soon you will notice emails or suggestions, showing you the same products or the ones that suit your taste. Indeed, this fine-tunes the shopping experience. Its machine learning that does the job. Based on your behaviour with the website or application, previous purchases, products liked or added to the cart & brand preferences, the system makes the product recommendations.

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