No doubt that Big Data is a major part of future technological development. However, machine learning (ML) and Artificial Intelligence (A.I) both play an important in this development. The relationship between these three is briefly explained: Big data is for materials, machine learning is for method, and artificial intelligence is for results.
What Is Machine Learning?
Machine learning (ML) is one of the types of artificial intelligence (A.I) in which algorithms are written in such a manner that the system is given the ability to automatically learn, adapt and improve automatically through the experience without explicitly being programmed.
The Machine learning algorithms build an exemplary model that is based on the type of data it is targeted to learn, this type of data is called “training data”.
Types of machine learning?
There are various types of Machine Learning Algorithms, it can commonly be divided into 4 categories different types of machine learning are as follow:-
- Supervised learning.
- Un-supervised learning.
- Semi-supervised learning.
- Reinforcement learning.
When the machine is being supervised while it is in its “learning” stage, this type of training is called supervised learning. What do we really mean when we say a machine is being supervised ?. What it really means to apply algorithms in such a way that allows the machine to learn to use its old data (data provided in the past) and use it to make predictions of future events surrounding the type of data entered i.e. old data.
The analysis is started and all the materials in the training dataset and labeled correlate with the machine in such a way that it can make a prediction of the correct output values. It means that we provide the machine with a lot of information about a particular case and then it provides a case outcome. The outcome is called the labeled data while the rest of the information is used as input features. The system can then also provide targets for new input after sufficient training. The algorithm can compare its output with the intended output and find differences to change the model accordingly.
Mostly this method is manual classification, which is the easiest to perform for a computer and the hardest for humans. An example of this method is, telling the machine standard answers, and when the machine is tested, the machine will always reply according to the standard answer and hence its reliability will also be greater.
In contrast to supervised learning un-supervised learning algorithms are used when the information which is used to train the machine is neither classified nor labeled, as the name suggests in unsupervised learning no help is offered from the user to the computer to help it learn.
The material provided has no label, and the machine then matches the characteristics of the data and classifies the materials. Due to the lack of labeled training sets, the machine then identifies patterns in the data which are not so obvious to humans.
In this method, there isn’t any manual classification, which is the easiest for humans, but hardest for the computer and can cause much more errors. The system mostly does not figure out the intended output, but it researches the data provided and can draw relations from datasets to describe hidden structures from unlabeled data. Hence, to recognize patterns in data unsupervised learning is extremely useful and it also helps us make decisions.
Semi-supervised learning is unlike supervised learning and unsupervised learning in which, either there are no labels present for all the observation of data or labels are present.
In Semi-supervised both labeled (supervised) and non-labeled (un-supervised) data is used for training. SSL is a mixture of the two types of learnings in which a small amount of data is labeled and large quantities of data is unlabeled. The machine is required to find features through labeled data and then using the base model it classifies other data accordingly. SSL systems can considerably improve not only their learning accuracy but can also make more accurate predictions.
It is the most commonly used method because the cost to label is high since skilled human experts are required. It requires relevant resources to train it and learn from it while acquiring unlabeled data generally does not require additional resources. Due to the lack of labels in the majority of the observations but the presence of a few, semi-supervised algorithms are preferred the best candidates for building a model.
These methods benefit from the idea that even though the group members are unknown because unlabeled data is more generally, information about the parameters is still carried in the labeled and can be found using it.
Reinforcement learning is the closest to how we humans learn. RML algorithms are a learning method in which the machine repeatedly interacts with its environment by constructing new actions and discovers errors or rewards. It uses a positive or a negative reward-based system.
Trial and error search with delayed reward is the most relevant characteristics of reinforcement learning. The machine constructs a behavior using observations gathered from interacting with the environment and takes actions that would maximize the reward or minimize the risk. This method allows the machines to automatically determine the ideal behavior within a certain context to increase its performance. In reinforcement learning, there are no labeled materials, but instead, it requires simple feedback that which step is correct, and that step is wrong, this is known as the reinforcement signal.
According to the standard of feedback, the machine gradually revises its classification until finally gets the correct result. Integration of reinforcement learning is necessary in order to achieve a certain level of precision in un-supervised learning,
RML is probably the hardest to produce and execute in a business environment, but it is being commonly used for self-driving cars.