None of us are born with the ability to drive. Driving is a process that we learn and we develop this skill by practice and from experience. Humans learn from experience. But what about machines, can we teach a robot how to drive and have a scenario where it can learn from experiences.? It is possible and in the case of machines, these experiences are called data. This is what machine learning is basically about.
Machine learning is the process of creating or construction of systems that can learn from the input data, recognize patterns and thus make an intelligent decision. We train them or they are taught in such a way that they can make an accurate decision when a similar situation arises in the future. Now often the terms machine learning and artificial intelligence are used interchangeably, but machine learning is a subset of AI. Artificial intelligence is basically nothing but a program that thinks and act like humans do. Even though both AI and ML are part of computer science, they are not the same thing.
While people may be unfamiliar with this term, we see applications of machine learning in our everyday lives. Let us consider a simple example, assume that we are asked to make an app that identifies the gender of a person when we point the camera towards it. We already mentioned that humans learn from experience and machines learn from data. So gathering data is the first step. In case of our example, the data collected will include the facial features of both genders that differentiates them like the shape of the eyebrows and jawline. The quality and quantity of the training data will directly impact how accurate the result or the prediction by the system. For example, if the shape of jawlines and eyebrows can be used for determining the gender we can prepare the training data based on this. This will be given to a model. Here comes the main part of ML. There are different types of models that can analyze these data whether it’s image or numerical data. Mathematics plays a key role in training these models. So it’s safe to say that mathematics and programming go hand in hand with training these models. Since here we are only considering two factors, the two shapes a smaller model will only be required. Thus over the time, these models will start to learn how to identify whether a person is male or female by recognizing and analyzing the two shapes. We can evaluate this by giving input which is not part of the training data to check the accuracy of the system. Finally, after all the training and evaluation we can give data to the model to make a prediction because after all machine learning is all about getting answers by giving data. This is the basic principle behind the process of machine learning.
Machine learning as the name suggests is the learning done by a machine to do a particular task or rather training a machine by providing the proper training data so that it can make prediction or decision when a similar situation is given. Checking whether a mail is a spam or not, setting an air conditioner etc are some simple applications of ML. But if the air conditioner is asked to set the room temperature based on the factors outside the room or house, it may not give the accurate result since it was trained on the internal room temperature factors. We can see a number of applications of ML in various fields nowadays. Be it in search engine optimization, medical diagnosis, gaming, ML is gaining more recognition and the scope of this particular branch is increasing leading to better lives and a better future.