Before looking at the various types of machine learning we should understand the kind of data that is fed to the machine for processing. in ML, we can broadly classify the data into two categories labeled data and unlabeled data.
labeled data is a data which has both the input and output parameters or attributes in a completely machine-readable pattern, but requires extensive human effort to label the data. unlabeled data, on the other hand, has only one or none of the parameters in the machine-readable form, this negates the application of human effort but requires more complex designing.
what are the types of machine learning?
SUPERVISED LEARNING: in supervised learning, the machine fed labeled data. even though the data needs to be accurately labeled for this type of learning, supervised learning is extremely powerful when applied in right circumstances.
We train the machine with the input and known output(training set), and then we ask the machine to predict the output using the test data set. this training data set is part of a bigger dataset and serves as a blueprint or an instruction manual helping the machine learn. At the end of the training, the algorithm has an idea of how the data works and the relationship between the input and the output. This means that supervised machine learning algorithms will continue to improve even after being deployed, discovering new patterns and relationships as it trains itself on new data.
Example – We have images of a cats and dogs (INPUT DATA SET). First we train the machine to understand the image using attribute such as shape of tail, shape of eyes, height, shape of ears etc. After completion of training, we input the picture of a cat and ask the machine to identify the object and predict the output.
UNSUPERVISED LEARNING: Unsupervised machine learning holds the advantage of being able to work with unlabeled data. This means that human labor is not required to make the dataset machine-readable. on being fed the training data the machine tries to form hidden relations between different data objects and predict the output, unsupervised learning algorithms can adapt to the data by dynamically changing hidden relations. This offers more post-deployment development than supervised learning algorithms.
The main aim of the unsupervised learning algorithm is to group or categories the unsorted dataset according to the similarities, patterns, and differences.
Example: suppose there is a basket of fruit images, and we input it into the machine learning model. The images are totally unknown to the model, and the task of the machine is to find the patterns and categories of the objects.
REINFORCED LEARNING: Reinforcement learning works on a feedback-based process, in which an AI agent (A software component) automatically explore its surrounding by hitting & trail, taking action, learning from experiences, and improving its performance.
The reinforcement learning process is similar to a human being; for example, a child learns various things by experiences in his day-to-day life. An example of reinforcement learning is to play a game, where the Game is the environment, moves of an agent at each step define states, and the goal of the agent is to get a high score. Agent receives feedback in terms of punishment and rewards.
-Positive Reinforcement Learning: Positive reinforcement learning specifies increasing the tendency that the required behavior would occur again by adding something. It enhances the strength of the behavior of the agent and positively impacts it.
-Negative Reinforcement Learning: Negative reinforcement learning works exactly opposite to the positive RL. It increases the tendency that the specific behavior would occur again by avoiding the negative condition.
SEMISUPERVISED LEARNING: semi-supervised leaning is a combination of supervised and unsupervised learning that is. it could work on both labeled data as well as unlabeled data and still generates desired results according to the problem statement.