What do we mean by the term machine learning? The definition is quite difficult, to do so, we are asking large field of scientists to help. We can mention an artificial intelligence pioneer's quote:
– Arthur Samuel
Machine learning is about training a model or an algorithm with data and then using the model to predict any new data. For example, a toddler is taught how to walk from his crawling phase. Initially, the toddler's parents hold the toddler's hand to help him up, and he is taught through the data that is given. On the basis of these procedures, if an obstacle presents itself in the toddler's path or if there is a turn somewhere, the toddler is able to navigate on his own after the training. The data used for training is the training data and the recipient continues to learn even after the formal training.
Machines too can be taught like toddlers to do a task based on training. First, we feed enough data to tell the machine what needs to be done on what circumstances. After the training, the machine can perform automatically and can also learn to fine-tune itself. This type of training the machine is called machine learning.
The main difference between machine learning and programming is that there is no coding/programming involved in machine learning, while programming is about giving the machine a set of instructions to perform. In machine learning, the data is the only input provided and the model is based on the algorithm we have decided to use.
The algorithm to be used is based on various factors of the data: the features (or independent variables), the type of dependent variable(s), the accuracy of the model, and the speed of training and prediction of the model.
Based on the independent variable(s) of the machine learning data, there are three different ways to train a model:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
The following figure shows the different algorithms to train a machine learning model:
In the following sections, we will go through them on by one.