Over the past few years machine learning has become pervasive and today almost all the organizations are using it in a variety of ways that includingreinforcing cyber security, boosting self-driving cars, safeguarding public safety, improving medical researchesand enhancing recommendation engines. To illustrate recently Apple bought Machine Learning Image Recognition Company Perceptio Inc., in order to bring image-recognition artificial intelligence to Smartphones by reducing data overhead.Here we will be learning about machine learning and its various applications in different domains.

About machine learning

Human beings have extraordinary capabilities of naturally sensing and recognizing their surroundings through which they observe the things and act accordingly. Recognizing images, understanding gestures, listening spoken words, reading texts, interacting with people, and distinguishing various other things are a part of human sensing skills. This is what machine learning image recognition technology solutions are trying to teach the machines.

Machine learning is an application of computer science that provides machines the ability to automatically learn and progress from experience without being explicitly programmed. It is a process in algorithms find patterns in data and then calculates the possible results. With the help of machine learning the email program is able to scan the messages that you receive and finds if the mail received is a spam. Organizations are using this technique to make new inventions, as well as to identify and resolve issues quickly.

The different categories of machine learning

Depending on the algorithm and its objectives, machine learning is divided into three mainareas that include supervised learning, unsupervisedlearning and Reinforcement learning.

1. Supervised learning-This type of learning develops a model that makes forecaststo predict the correct outcome based on the indication in the presence of uncertainty and introduction of new examples. Just as a student learns from the teacher similarly in supervised learning machine learns through examples. With the help of supervised learning a patient can get heart attack or not can be predicted.

Under this type of learning classification and regression techniques are used to create models based on predictions. Classification method envisages discrete responses for example to know the received mail is a spam or not. Classification technique is generally applied recognizing medical images, speech recognition, and credit scoring.

On the other hand regression technique predicts continuous responses for example fluctuations in the demand of power, or variations in temperature.This type of method is generally used in electricity load forecasting and algorithm trading.

2. Unsupervised learning-This type of learning comes into play when a machine learns on its own through simple illustrations without using any related response or examples to find hidden patterns or structures in data. It is used to draw conclusions from data sets that consist of input data without known responses. The most common Unsupervised learning technique is clustering that is used for exploratory data analysis to find hidden patterns or structure in data.This type of method is generally used in gene sequence analysis, object recognition and market research. To illustrate the use of clustering in real-world mobile phone companies’ use clustering to know in which areas they have built their mobile phone towers and estimate the number of clusters of people that are depending on that particular tower.

3. Reinforcement learning- This type of learning is to some extent similar to unsupervised learning. However under this positive and negative feedback can be given according to the solution the algorithm recommends. Trial and error is most common technique used under reinforcement learning. This type of method is generally used in video games making industry. Reinforcement learning is connected to applications for which the algorithm must take decisions and the results bear the consequences.

Importance

Machine learning has plethora of practical applications that is flourishing the business growth in today’s competitive world. Many activities that were once ago thought to be under human domains alone are now being accomplished with the help of machine learning image recognition systems. Since machines now will be capable of performing far much better and faster than human beings, it will show a huge impact on the whole economy, politics, and society. With the help of machine learning image recognition technology solutions almost all the industries will work automatically and will progress much faster leaving behind hundreds of thousands of people out of work. Moreover people have gained the ability to get things done more quickly and faster ever than before.

Toboost your businessplanning effective machine learning systems is very essential and it needs superior data preparation abilities, scalability, automation and iterative process, knowledge of basic and advanced algorithms, and knowledge of ensemble modeling.

Its Applications

These days several big organizations have realized the worth of using machine learning to control cost and overcome competitive advantage and hence are suing it widely to grow their business.

1. Government- Utilities and public safety government agencies are in greater need of machine learning as they have large variety of data sets that can be excavated for recognizing useful patterns and insights. For example MI can be used to minimize thefts and fraud that are occurring in the society.

2. Marketing and sales- Companies are in need of machine learning solutions in order to analyze the past purchases done by their customers and based on the results can make product recommendations for their next purchase.

3. Healthcare- Machine learning has become a fast-moving technique in healthcare industry as with the use of data patients real health can be determined correctly. Sensors and devices that are being used in the health-care facilities are able to detect the health of the patient by using image recognition techniques. For example with the help of ML it can be diagnosed if a patient can get heart attack or not.

4. Transportation- With the help of machine learning transport companies can predict the problems that may arise on certain routes and then advice the customers which route to opt for. Companies are enabling customers to resolve their problems while they are travelling.