What is Machine Learning and How It can Change Our Future ?

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What is Machine Learning?

Machine Learning (ML) is the study of computer algorithms that improve automatically through experience. ML is technically a branch of Artificial Intelligence, but it is more specific than overall concepts.

[1]  It is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

[2]  It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data. It is known as “training data”, to make forecasts or decisions without being explicitly programmed to do so.

[3]  Its algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop proper algorithms to perform the needed tasks.

[4]  The process of learning begins with observations or data. Such as direct experience, or instruction. To look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human interference or assistance and adjust actions accordingly.


Machine Learning is, in part, based on a model of brain cell communication. The model was created in 1949 by Donald Hebb. It is created in a book titled The Organization Behaviour. The book presents Hebb’s theories on neuron activity and communication between neurons.

Hebb wrote, “When one cell repeatedly assists in firing another, the axon of the first cell develops synaptic knobs (or enlarges them if they already exist) in contact with the soma of the second cell.” Translating Hebb’s concepts to artificial neural networks and artificial neurons, his model can be described as a way of changing the relationships between artificial neurons (also referred to as nodes) and the changes to individual neurons. The relationship between two neurons/nodes strengthens if the two neurons/nodes are activated at the same time. And weaken if they are activated separately. The word “weight” is used to describe these relationships, and nodes/neurons tending to be both positive or both negative are described as having strong positive weights.


Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results. Even on a very large scale. And by building specific models, an organization has a better chance of identifying valuable opportunities – or avoiding unknown.


types of machine learning

Supervised Machine Learning Algorithms:

In supervised machine learning, The machine is trained by the user of the previous data record. We give labels to the previous data. We also give result to the machine so the machine can analyze the previous record and divine the future outcome. In machine learning period the data which we give to the machine is known as the dataset.

Unsupervised Machine Learning Algorithms:

In unsupervised machine learning, we give dataset to the machine but here we don’t give a label to the data. Don’t provide the result of the data to the machine during the training time of the machine. So in this situation, the machine makes a group of the dataset. The data which are enrolled in one same group, have the same property and features. And by the help of these group machine predicts the result in a group form.

Reinforcement Machine Learning Algorithms:

In reinforcement machine learning, machine continuously working a directly takes motivation from how human beings learn from data in their lives. It features an algorithm that improves upon itself and learns from new situations using a trial-and-error method. In case of the finding best or correct solution, interpreter reinforces the solution by providing the reward to the algorithm which is work behind it. if in any case result is not helpful then interpreter asks the algorithm to repeat it until you find the best result.

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