The evolution of computers and developers plays a vital role. With exponential gradation in technology, what more can you expect from the next level after an era of computers and tremendous programming? The answer for this lies in the concept of “Machine Learning.” This article will explain the concept and scope of Machine Learning in simple words to students of technical or non-technical backgrounds.
In traditional programming, we process data through a program in a computer to produce an output. In machine learning, data and outputs are run on a computer to create a program and get the results. The whole story revolves around “Let data do work instead of people.” And it becomes more powerful when we have more data for learning. Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data. It identifies patterns and makes decisions with minimal human intervention.
How does it work?
Machine learning uses two techniques to train a model to get predicted output as a result. These two are Supervised and unsupervised Machine Learning.
Supervised Machine Learning
It builds a model that makes predictions based on evidence in the presence of uncertainty. This known set of input data and responses to the data trains model to generate reasonable predictions for the response to new data. Supervised learning is used if we have known data for the output we are trying to predict. It further uses two techniques as discussed below:
– Classification Techniques
The classification model classifies input data. Typical applications include medical imaging, speech recognition, and credit scoring. It is useful to tag, categorize, or separate into specific groups or classes. For example, whether an email is genuine or spam or whether a tumour is cancerous or benign.
– Regression Techniques
Regression is useful if we are dealing with a data range. We can say the nature of your response is a real number, such as temperature or the time until failure for a piece of equipment. Typical applications include electricity load forecasting and algorithmic trading.
Unsupervised Machine Learning
It draws inferences from datasets consisting of input data without labelled responses. One of the most common unsupervised learning techniques is clustering.
For exploratory data analysis to find hidden patterns or groupings in data clustering is useful. Applications for cluster analysis include gene sequence analysis, market research, and object recognition. For example, a cell phone company wants to optimize the locations where they have built cell phone towers. They can use machine learning to estimate the number of clusters of people relying on their towers. So, these algorithms are used to design the best placement of cell towers for optimizing signal reception for groups or clusters of their customers.
Scope of Machine learning
Machine learning solves problems by making predictions for certain outcomes, like the probability that a certain user will click on a certain kind of ad and many more. It exhibits wide scope through which it can solve many problems. The use of big data is already finding new waves of productivity growth and consumer surplus. And with its rise, machine learning can solve problems in every field. Two of these are discussed below:
Natural language processing
Through NLP, developers can organize and structure knowledge to perform tasks. Such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, topic segmentation etc. Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples and making a statical inference. The more data analyzed, the more accurate the model will be.
Computer vision and Image Processing
Computer vision and Image Processing deals with making computers see gestures and the ability to understand the meaning of that, respectively. The given pattern is matched to computer knowledge which is constructed with the help of machine learning. Hundreds of samples are trained so that system should be capable of detecting and understanding subtle differences in movements.
Apart from the above-mentioned applications, machine learning is useful in Computational Finance, Computational Biology, Automotive, Aerospace, Manufacturing and Energy production.
Machine learning algorithms detect natural patterns in data that generate insight and help make better decisions and predictions. That is useful for making critical decisions in medical diagnosis, stock trading, energy load forecasting, and many more applications. But the robust availability of data for machine learning is critical. In a nutshell, we can conclude that machine learning can widen a business platform without people intervention. Or, in other words, giving a special place to modern tools for upgrading the system.