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Supervised Learning
- What is Supervised Learning?
As the name indicates it has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using a data which is well labelled. It means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples i.e. new set of data so that the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labelled data.
We can think of supervised learning as a teacher supervising the entire learning process. It’s one of the most common ways that a machine can learn, and it’s an invaluable tool in the field of Machine Learning. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.
In short we can say that, it’s a machine learning strategy that enables Artificial Intelligence systems to learn and make progress.
- Why supervised learning is important ?
- Supervised learning gives the algorithm which can be used to output the predictions for new and unseen data.
- It also helps in optimizing the performance of the algorithm.
- Real-world calculations can also be taken care of by the help of Supervised Learning algorithms.
- Types of Supervised Learning – Supervised learning problems are further classified into regression and classification problems.
- Regression
- Classification
v Regression – Regression is the kind of Supervised Learning which learns from Labelled Datasets and after learning is able to predict a continuous-valued output for the new data which is given to the algorithm. It is used whenever the output required is a number such as money or height etc.
- Classification – Classification is the kind of Supervised Learning where the algorithms needs to map the new data which is obtained to any one of the two classes that we have in our dataset. The classes need to be mapped to either 1 or 0 which in real-life translated to “Yes” or “No”, ”Sunny” or “Not Sunny” and so forth. The output will be either one of the classes and not a number as it was in regression.
Applications of Supervised Learning –
Supervised Learning algorithms are used in a variety of applications.
- Bio-Informatics – It is the one of the most well known applications of Supervised Learning because most of people use it in day-to-day lives. It is the storage of Biological Information of humans such as fingerprints, iris texture, earlobe and so on. Mobile phones of today are capable of learning our biological information and are then able to authenticate us bringing up the security of the systems. Smartphones such as iPhones, Google Pixel are capable of facial recognition while OnePlus, Samsung, Vivo is capable of In-Display finger recognition.
- Speech Recognition – This is the kind of application where you can teach the algorithm about your voice and machine will be able to recognize you. The most well known real world applications are virtual assistants such as Google Assistant and Siri, which will wake up to the keyword with your voice only.
- Object Recognition for Vision – This kind of application is used when you need to identify something or find something. You have a huge dataset which you use to teach your algorithm and this can be used to recognize a new instance.
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