How Machine Learning turns Big Data into functional data?

We recently had a write-up on Big Data stating how powerful Big Data is to transform business for industries. However, we indolently missed to bring to the attention of readers about a single, mighty technology – Machine Learning, the technology that makes Big Data a handy tool for business entrepreneurships. And this story covers many important aspects everyone wants to know in transforming business models. In fact, we are all living in an unprecedented world of technologies where transformations happen and cause the denouement of many and germination of others keeping up the equilibrium of entrepreneurial culture.

Machine learning is bestrewed across engineering, sciences, and commercial applications. In brief, Machine Learning acts as an effective tool for using unstructured and semi-structured data in the most prominent way which are available in different forms from different sources. In this blog, we will be discussing how and from where Big Data came from and how the term ‘Machine Learning’ originated?

It is all about data

Data are distinct pieces of information that are generated from structured or cleaned sources. Data are spread in different forms and types. They are predominantly categorised into unstructured, semi structured, and structured data, that are in forms of numbers or text on pieces of papers, as bits and bytes stored in electronic machines, and facts stored in a person’s mind.

In the perspective of predictive analysis, data is the source for extracting information. By analyzing data, we can forecast the future. When we can derive patterns or functions from data, we can also predict the future using the pattern or function. Informations are generated from the structured or cleaned data. Informations are lately formed to become the stepping stones to the future.

How data became big data?

Data gears up from many sources. During the gradual soaring changes in the sources of data, it’s quite habitual to witness an elevation in the volume of data. Social media channels, sensors that communicate, employees, e-commerce, online banking, and electronic machines generate a huge amount of data. It turns data from gigabytes to exabyte storage spaces. This enlargement in the source of data or the collection of data irrespective of its relevance reasons the formation of big data.

How Machine Learning works with Big Data?

Now there is a huge amount of data. Analysing this collection of data we can study the past and can also generate a function to forecast the future. Forecasting the business logic is the leading technique to improve business with better strategies. Now a days, it is required to look back to the past and study what happened in the past. By analysing the past studying the characteristics of the data will help you to create a pattern to predict the future, which is Machine Learning. Pattern analysis and creation of mathematical models are the core functions in this process. Analysing the huge amount of data from past sources, recognising patterns and create a mathematical model from such patterns we can forecast the future of the business product.

By analysing the big data we can find the features of data, and classify data into different classes using different types of classification algorithms. Using the same pattern, we can predict what the world was like. On the other hand, we can derive a pattern or a mathematical model to forecast the future using regression and correlation algorithms.

Briefing Machine Learning

Machine learning is artificial intelligence that provides self learning ability to systems from the huge amount of data so that systems can analyse the past and predict the future trends using data. Machine learning advanced from the study of pattern recognition and computational learning theory. This makes computer programs to learn themselves to act accordingly when mated with new data without being explicitly programmed. Machine Learning is also known as predictive analytics.

What makes Machine Learning a handy is that it reduces man power and does several things that only human resources had been practising for centuries. However majority are just stuck at the baby stage. Certainly, we will experience a future flocked with Machine Learning. Let’s check them below.

Image prediction

Auto-captioning images is one of the stunning applications of Machine Learning. Microsoft’s BLUE technology is one of the well renowned tools in this category that is capable of ensuring system captions which are even better than human captions.

Age guessing

Now even smartphones do age guessing. Xiaomi devices are widely known for the same when posing for selfies. It detects the gender and displays the age of the user that are somewhat perfect.

Used vehicle valuation tool

Helps you to value used vehicles to get its most approximate price considering the model year, analyzing the problems it can have, stating the condition etc. With a huge amount of data, it helps you to give a brief on what all issues a used vehicle would have of a particular model year.

Social media updates

Have you ever noticed on Facebook that you’re receiving updates on your home page only from few profiles and pages, and missing many updates from your friends’ list and following pages? It’s just due to Machine Learning. That is, when you like or comment someone’s or some page’s photos or posts, the system analyses your activity that you’re mostly connected to such profiles or pages. So, it principally fills up your wall prioritizing with updates from those particular profiles or pages.

Handwriting Recognition

Handwriting recognition was earlier predicted as a wake-up call to the Machine Learning community. It is as simple as a system recognition to handwritten alphabets and numericals on smartphone touch screens. Handwritten inputs are created or converted from sources like photographs, paper documents, touch screens and other devices.

Video subtitle generation

We know the traditional making of movie subtitles are too laborious. But, with Machine Learning it can be automatically done. Translation is also possible here. You could choose your preferred subtitle language. This saves both time and money for film makers.

Voice recognition

Machine Learning helps you to recognize famous voices. For instance, if you train the system with a load of voices of Narendra Modi, Pm, India; the system generates a voice print. The next time when a clip (video or audio) is given to scan, the system scans with the already generated voice print and detects the voice of Mr. Modi.

Translation

Translation is one of the most common Machine Learning applications used today. The most accurate language translation happens when you have more data.

The future

There is a lot left to be done via Machine Learning to explore Big Data to its heights in the coming days. Many of the applications pointed above are just in the baby phases. Distinctly, the future will be driven by those applications and technologies. However, many are yet to appear. Let’s check out on such few innovations.

Exam paper valuation

With this Professors won’t be stressed to sit hours and hours to examine papers like present. The artificially intelligent system mated with Big Data via Machine Learning will be much capable to value your exam sheets more accurately that human Professors or Lectures. Presently there are systems that value exams written over computers and tablets. But, they are mostly objective questions. However, the all new application which is still under researches will be proficient to evaluate hand-written paper sheets and subjective answers in both electronic machines and papers.

This is not the end of futuristic applications of Machine Learning. The future is much more with Machine Learning APIs (Application Program Interface) as it strives into the complexity of algorithms that are conceptual and scalable; making predictions from the data helping in the complexity associated with Machine Learning infrastructure stitching traceability and repeatability. Machine Learning will be a more powerful tool in the coming days as it will change the track from algorithms to specialization, composability, standardization, and automation and will result in bringing a better, easier, faster, and cheaper industrial standards with more productivity.