What is Machine Learning?

Machine learning is a widely used term in the IT world. The latest technological advances in Artificial Intelligence, commonly known as AI, allow computers to go on automatic learning mode through machine learning. Want to know what is machine learning? Here is brief know-how about what is machine learning and how does it help.

How is Machine Learning Defined?

An artificial intelligence application, machine learning aims for making computer learning automatic. In simple terms, machine learning helps systems to learn automatically and improve by experience. Such computer programs that can access data and may be used by computers to learn themselves are developed under machine learning. This form of cognitive computing is not only limited to developing systems, but also promises of transformational changes in the IT world.

A simple example of machine learning in everyday life is the ridesharing app Uber. Ever wondered how they detect the number of taxis in your vicinity, how much time will it take to reach the destination or how prices rise during peak hours? This is nothing but machine learning combined with other technologies such as artificial intelligence, predictive analytics and natural processing language. The food delivery apps also run on similar machine learning concept.

How does Machine Learning Work?

To begin with, the process begins with learning through data or observations. These may be examples provided by us to the computer in order to learn how to take the best decisions in similar situations in future. Machine learning focuses on developing inbuilt programs. Direct instructions or direct experiences are also used as learning material to gain information about how to look for useful data patterns.

Supervised and Unsupervised Methods

Machine learning algorithms may be supervised or unsupervised. In the supervised category, algorithms apply past learned data to new events according to labelled examples. On the other hand, unsupervised algorithms use unclassified or unlabelled information. The system explores data and draws inferences by describing hidden structures from unlabelled info. There are even semi-supervised methods wherein both types are mixed in varying proportions. Then, there are reinforcement algorithms that interact with the environment and detect errors in order to enhance its performance.

Machine learning helps to manage and analyse huge quantities of data. While it drives data reading faster, it gives accurate results to identify advantageous opportunities as well as risks.

Machine learning Algorithms overview

 

Machine learning is a fuel for artificial intelligence. Artificial intelligence is the ability of machines or robots to think and reason like human beings. With the help of machine learning, it is possible to provide this ability to artificially intelligent machines. It is a statistical technique that enables the study of pattern recognition from interpreting copious amounts of data generated by machines. Machine learning enables learning from this data and imparting the knowledge to the machines to operate tasks earlier done by human beings only. Due to its growing importance, many youngsters want to enrol in a machine learning course and start a career in this field.

Role of Algorithms in Machine Learning

Machine learning is a field of study that gives computers the ability to learn from previous data without being programmed into doing a certain set of actions. It does so by constructing the algorithms that can learn from data as well as interpret this data to make predictions about related events in the future. It then develops a model using the input observations and makes data-driven output predictions on the basis of that data.

Algorithms are specific directions on how to solve a problem. It has application in mathematics and computer science. In machine learning, there are several algorithms that are being used for finding solutions to different problems. The algorithms have to be appropriate for a specific machine learning task. In machine learning, algorithms are defined as learning a target function that can best map the input variables to an output variable. In layman terms, machine learning algorithms help in making a set of predictions in the future by interpreting the examples of the present.

The most basic type of machine learning algorithm is Y=f(X) where Y is the prediction done on the basis of X. It is also known as predictive analytics. The goal of any algorithm is to make the most accurate predictions on the basis of given data. There are different types of algorithms which are currently in use such as linear regression, logistic regression, linear discriminant analysis etc.

Algorithms are critical to machine learning. The type of algorithm chosen can depend on the size and quality of data, the availability of time, the urgency of the task or the business requirements. Only with the correct set of algorithms, can machine learning achieve its task.