There are lots of abbreviations used in the Computer Science domain for various technologies such as HTML, HTTPS, API, etc. However, sometimes we have a common abbreviation for distinct tools or technologies that often confuse the individuals (especially the beginners). For instance, IDE stands for both – Integrated Development Environment and Integrated Device Electronics. There are several other examples also. And with the same concern, here in this article, we’ll take a look at ML that implies Meta Language as well as Machine Learning.
Let’s first talk about ML – Machine Learning:
Artificial Intelligence is a growing trend these days and hence its branches Machine Learning, Computer Vision, Natural Language Processing, Deep learning, and so on. Among all these, Machine Learning is a segment of AI that enables a machine to learn things and improve accordingly. The applications of Machine Learning are in almost every discipline such as Image Recognition, Speech Recognition, Healthcare, and many more.
Machine Learning technology uses statistics and data to learn how to do an assigned task. Here, the humans have to provide a set of data to the machine. The technology then works on developing a computer program and initiating self-learning, by accessing the provided data. In this way, machine learning enables computers to perform tasks without human interventions. There are different ways to carry out Machine Learning:
Supervised Learning: Supervised Learning is concerned with the learning method in which the machine is trained with the well-labeled data that implies some data is already tagged with the right answer. Then the system is provided with a new set of data to predict the correct outcomes using the learning algorithm.
Linear regression and classification are the best examples of Supervised learning. Finding the price of a house is an example of linear regression if the dependent variables used are such that the size of a building/house increases with the number of people living there and thus the increase in the size of the building results in the increase in house prices.
In classification problems, we can predict different class labels (Yes/No, Class A, B, C, etc.) based on the input data. Some basic examples of Yes/No problems include grouping emails as spam or not, checking the result of virus infection as positive or negative, etc. Other examples include grouping cars as family cars or not, classifying different animals and bird species, etc. All of these are examples of supervised learning.
Unsupervised Learning: Unsupervised Learning is concerned with the learning method where there is no prior knowledge or training and no teacher is provided for the training. Previously labeled responses or target output is absent in unsupervised learning. Here, the inferences are made from the input data. The human supervision rate is low in the case of unsupervised learning and the model or machine is made to self-work to generate inferences. That means the model will work on its own to discover the patterns and information.
Reinforcement Learning: Reinforcement learning is also a machine learning paradigm like supervised and unsupervised learning. Here we have an agent and an environment. In Reinforcement Learning, the system is required to learn from its own experience with the help of a reinforcement agent. The correct answer is not known to the system priorly and the reinforcement agent decides what to do with the provided task itself. The agent is rewarded for the performed actions. This learning category is all about how the software agents take actions in an environment to maximize the rate of the cumulative reward earned. It is often used for predicting the winner of chess problems and other games.
Now, let’s move to ML Language – Meta Language
Meta Language is a general-purpose programming language that belongs to the Functional programming family. The Meta Language generally represents the language of manipulating program. It is often called a multi-paradigm, impure functional language as it showcases a few side effects. The language has a garbage system that facilitates automatic memory management. The language also helps to describe the objects, grammar, and concepts associated with a programming language.
The descendants of ML include Miranda, Haskell, Caml, F#, etc. All these languages make it easy to define the curried functions. The ML is an interpreted language and the variables are statically scoped in Meta Language. Moreover, Meta Language provides a number of built-in functions and each function in ML expect an argument of a particular type and returns the particular type of result. Also, the operator fun is used to define the functions and the function defined using fun can be recursive as well.
The applications of Meta Language include bioinformatics, compiler design, automated theorem proving, financial systems, and so on.
As of now, you must have understood the difference between Meta Language and Machine Learning!!