What Is Machine Learning & Its Applications ?
For many years , industries are finding ways to implement artificial intelligence(AI) in their working to ease on work and be ahead from their competitors . As it is emerging technology , the exploration of AI has brought about sub concepts . Machine learning is concept of AI only , which is a computer with ability to learn without being programmed .
Machine Learning
We can define machine learning (ML) as subset of AI wherein algorithms of computer is being used to automatically learn from data and information present , without been specifically programmed .
Once the information and data is collected , system has ability to change and improve its overall algorithms by its own . The learning algorithms facilities system for identification of patterns on the basis of information and data collected and thereby building predictive models on the basis of observation collected . ML is used in a situation wherein prediction is not expected , with aim to achieve predictions that will be useful .
Nothing is simple is tech world and so is the case with machine learning . Similarly the concept of ML is further broken down into small subsets on the basis of amount of data collected and then the system provided : supervised learning , unsupervised learning and reinforcement learning . The type of learning is further determined on the basis of information it has fed in labeled or not .
Question that might be arising in your mind is how actually the system undergoes training . It is type of ML that will determine how much training your system has to undergo . So the training amount will be determined by amount of data system has provided initially . Data is heart of ML , and hence without it system will never be able to know how to do its job . Before proceeding any further , let’s first discuss what exactly is ML .
Supervised Learning
Supervised learning is part of ML wherein the algorithm input ( x) and their respective output ( y) are correctly labeled on the basis of data initially being provided by system . As both input and output data is correctly labeled , system is auto trained to recognise pattern in data with algorithms . The data collected then facilitates systems to receive inputs and give correctly labelled output on the basis of pattern . Supervised learning is proven benefits when you are willing to identify future input of data with no human interference . The best example of use of this technology is Facebook which help you identify faces of people uploaded when you tag specific people photo on Facebook .
Unsupervised Learning
Usually in unsupervised learning , data is fed to system but outputs are not labeled as in case of supervised learning . In unsupervised learning , system initially observe the data and then determine the pattern with the given information , rather than being trained in advance to recognise the pattern . Once the system is done with pattern recognition , it defines future inputs on the basis of pattern to produce an output . Unsupervised learning is being used in social media platforms to recommend friends to follow on the basis of algorithms that if you have studies from x university and other are also studying in same university with same year , they will suggest you friends to follow accordingly.
Reinforcement Learning
We can define reinforcement learning as subset of unsupervised learning . Like in unsupervised learning , the data provided to system is not labeled and hence system is left to create its own patterns. The major difference between the two is when correct output is produced , then system labels that output as correct . This therefore facilities systems to learn from its environment and explore range of possibilities arising out of it . For instance say when Spotify recommends you song , they facilitate you with an option to either “ thumbs up “ or “ thumbs down “ and then utilises your input to learn your taste of music .
Machine Learning Applications
So hearing a lot about machine learning now a days ? Well the reason being it is next step in achieving AI and is thereby big step for app developers . ML empower apps with ability to adjust on the basis of data collected from users , without any manual interference of developers . This technology therefore saves time of developers and at the same time enrich user experience as well .While there are numerous uses of machine learning applications , two most important uses are image processing and predictive analysis .
Image Processing
Image processing is made possible using ML , that you might be experiencing every other day . It uses supervised learning algorithms to detect various objects in given images . In this machine is trained by using set of labeled images which contains different objects . When future inputs is received , machine is capable enough of identifying those inputs and then label accordingly . Apple’s Face ID can be termed as best example of image processing .
Predictive Analysis
Another most famous use of ML is predictive analysis which showcase predictions on the basis of historical data . This includes suggestion for the words you will be using while typing text . They usually record pattern actively being used by you and then suggest you with response in future .
Machine learning is making huge impact on mobile app development . Right from recommendation on social media to unlocking your iPhone , machine learning has made our life easy in numerous ways . Developers are able to get user behaviour realisation . At the same time ML is also contributing in increasing security so that user feel safe while using your product . Contact Winklix products managers to implement ML in your solution .
