Machine learning is a smaller field within Artificial Intelligence. The general aim and goal of machine learning is to simply breakdown data and comprehend it fully, then utilise the knowledge in setting up systems and models that can be used by people in their daily activities.
Despite the fact that it is found within the computer science field, it is a drastic shift from the traditional standpoint of much of the more conventional fields. Much of the typical models are dependent on algorithms, which are standardised lines of code that provide instructions to the computers on problem solving. Machine learning algorithms on the other hand provides for means and ways for the computer to learn and prepare statistical points from the data input, thus to provide output values that are within a specified range. Much of the prediction points are wholly based on the experience of the machine with the data availed to the computer.
We live in what would be referred to as the primitive age of machines and the future that technology and computers promise is far beyond our wildest imaginations. Part of the divers towards this is machine learning. Much of the machines that have been in use in traditional computing had to be programmed to only perform certain functions and task, but now there is the possibility of allowing them to learn and train on how to work and feel like us and still produce more accurate results than any human labour ever could.
The machine learns by putting in the training data, which is utilised in creating a very particular model. Once this has been affirmed in the machine and its data, any other input will be processed depending on the previous models. The predictions made by the machine are wholly base on the experience the machine had with all of the previous data. Once there is an output all of the data is put into a process of trying the accuracy and dependability of the information, if it is reliable, then it is deployed, if not the machine is re-trained again with more data and better models are regenerated. This process is repeated until the most accurate predictions are made.
One of the areas this is well utilised in our daily lives I online commerce and in advertisements. Much of the adverts we receive on our computers or phones are based on our either previous searches or purchases. More often than not, we also receive recommendations of products that other buyers also bought together with whatever we are looking at. Much of this is fully based on machine learning and prediction.
There are various forms of machine learning including:
Supervised Learning – This is guided learning that is done by the dataset at the input.
Unsupervised Learning – The model looks into the patterns and inter-relations and divides up the data into sets though it cannot label and distinguish them
Reinforcement Learning – This based on a hit and trial method. With success, there are awards and punitive action upon failure. That way a final model is generated according to the requirements for the machine.
