Machine Learning Overview

Undoubtedly, Machine Learning is the most in-demand technology in today’s market. Its applications range from self-driving cars to predicting deadly diseases such as ALS. The high demand for Machine Learning skills is the motivation behind this blog.

In this blog on Introduction to Machine Learning, you will understand all the basic concepts of Machine Learning and a Practical Implementation of Machine Learning by using the R language.

What is Machine Learning

In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning.

How does Machine Learning work

A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately.

Machine Learning is a system of automated data processing algorithms that help to make decision making more natural and enhance performance based on the results. The “learning” implies that the algorithm can glean new information and insights without being explicitly programmed. There are several models of machine learning:

Supervised ML (the outputs are labeled, and the inputs are mapped to corresponding outputs)

Unsupervised ML (the inputs are unlabeled, and the algorithms have to find patterns)

Reinforcement ML (similar to supervised ML, but in this case, instead of a labeled output, there are rewards and the algorithm’s goal is to maximize rewards)

Features of Machine Learning:

· Machine learning uses data to detect various patterns in a given dataset.

· It can learn from past data and improve automatically.

· It is a data-driven technology.

· Machine learning is much similar to data mining as it also deals with the huge amount of the data.

What is the future of machine learning?

While machine learning algorithms have been around for decades, they’ve attained new popularity as artificial intelligence has grown in prominence. Deep learning models, in particular, power today’s most advanced AI applications.

Machine learning platforms are among enterprise technology’s most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, data classification, model building, training and application deployment.

As machine learning continues to increase in importance to business operations and AI becomes more practical in enterprise settings, the machine learning platform wars will only intensify.

Continued research into deep learning and AI is increasingly focused on developing more general applications.

Today’s AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and are seeking techniques that allow a machine to apply context learned from one task to future, different tasks.

Are you interested to learn Machine Learning? Check here the Machine Learning Topics