Machine learningWe are going totoward the time zone of artificial intelligence where everything is controlledand handled by the machine. Machine learning is the subset of artificialintelligence where we teach the machine to learn by itself without the help anyexternal source. In machine learning we teach the machine to learn from itsprevious data and try to improve its result in future by taking lesson from itsprevious decision. Part of machine learning includes the uses of tools, methodsand techniques which help it form better results. These methods and algorithmsprovide machine and us a new approach to explore the new knowledge from andgiven data or the by exploiting the traditional datasets. In some situation, wetry to record the behaviour and then model that behaviour.
In turn modellingstimulate the people to have a better understanding of the situation. Machinelearning method have a slight have a history of statistics. Its helpful forexploring more complicated learning model to take out the true message hiddenin large amount of data.
Although both machinelearning technologies and traditional statistics tools can be applied in dataanalysis, their fundamental principles and characteristics have a greatdifferent. As compared to statistics data analysis, the exclusive advantages ofmachine learning includes enumerated benefits which are : we can process bigdata and real-time data streams with mixed values types, we can select fromdifferent learning models and controlling parameters to capture the non-linearor high-order structure in data, we can also recognize complicated patternsthat cannot be represented in different mathematical terms, visualization ofthe data for making a prediction and we can also integrate the learning modelswith other different databases management system.Learningis a continuous process but in machine also we try to stimulate this learningprocess alike humans.
For machine basically learning consist of 3 types whichare supervised, unsupervised and reinforcement learning. All these play a majorrole to designing a learning models. Where First, in supervisedlearning, the computer is provided with example inputs that are labeled withtheir desired outputs.
The purpose of this method is for the algorithm to beable to “learn” by comparing its actual output with the “taught” outputs tofind errors, and modify the model accordingly. Supervised learning thereforeuses patterns to predict label values on additional unlabeled data. A commonuse case of supervised learning is to use historical data to predictstatistically likely future events.
Second, In unsupervised learning, data isunlabeled, so the learning algorithm is left to find commonalities among itsinput data. As unlabeled data are more abundant than labeled data, machinelearning methods that facilitate unsupervised learning are particularlyvaluable. In Third, Reinforcement Learning – a goal-oriented learning basedon interaction with environment.
Reinforcement Learning is said to bethe hope of true artificial intelligence. And it is rightly said so, becausethe potential that Reinforcement Learning possesses is immense. It allows machines and softwareagents to automatically determine the ideal behavior within a specific context,in order to maximize its performance. Simple reward feedback is required forthe agent to learn its behavior; this is known as the reinforcement signal.