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Introduction to Machine learning

Machine learning introduction: Here, we are going to learn about the introduction to machine learning.
Submitted by Aditi S., on June 04, 2019

What is Machine Learning?

Machine learning can be vaguely defined as a computers ability to learn without being explicitly programmed, this, however, is an older definition of machine learning. A more modern definition was given by Tom Mitchell, "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."

For instance, let's assume we have an algorithm that watches emails a user marks as spam and based on that observation it learns to filter out unwanted spam messages. The experience E in the above situation would be to Watch and recognize what type of mail is marked as spam. The task T would be to filter mail as spam based on the experience E. The Performance P would is the efficiency at which the algorithm filters spam mail and it would simply improve with the experience E.

Machine learning is often confused with Artificial intelligence. Artificial intelligence is measured as the ability of a machine to behave as a human being whereas Machine learning is a subset of artificial intelligence that deals with training a machine or computer to learn from large amounts of data supplied to it.

Machine learning is implemented in two ways, Supervised and Unsupervised learning.

Supervised learning is when the machine is given a specific data set along with the correct output. Here the machine is given an idea of what the output must look like with respect to the given input. Supervised learning is further classified into two subsets namely, Regression learning problems and Classification learning problems.

In a regression learning problem, we try and obtain predictions as a continuous function of the given input and not as a discrete value whereas in Classification learning problems we try to obtain a discrete value of the output based on previously analyzed data and the given input.

In classification learning problems, on the other hand, we approach problems without any knowledge about the correct output. The required relationship between the given data and solution can be acquired by clustering the given data based on the relationship of the individual variables present in the given data.

Machine learning is used and implemented in various fields of application. Most of us use machine learning algorithms unknowingly in our daily lives. Some of the common applications of machine learning are, Social media services such as personalized social media and news feeds by the content is being searched for, advertisement targetting and product recommendations by monitoring products or services viewed online, email and malware filtering by monitoring the content marked as spam and content classified as malware by users, Refining search engine results to improve search result by monitoring the time spent visiting and viewing web results, personalizing home and voice assistants by monitoring users internet and web activity. Machine learning is an important aspect to predicting highly accurate solutions to problems in various fields of applications such as science, medicine and commerce and can be employed to simplify and improve the quality and rate at which problems are solved.


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