What is a Machine Learning Algorithm?

Before understanding the concept of machine learning algorithms, we must first understand the concept of machine learning. Put simply, machine learning is a field of study in which we endeavor to teach computers cognition–the ability to reason, understand, and adapt beyond their explicit programming. The concept is closely related to that of artificial intelligence, where high-level mathematics simulate the patterns of thinking by making decisions based on gathered information rather than preordained choices.

The aforementioned high-level math in machine learning takes the form of algorithms, which are complex instructions written as formulas that the computer uses to make predictive “decisions”. Machine learning differs from simple processing by absorbing data sets to form models (test or example sets based on previous input data), which allows the computer’s brain to make dynamic choices that evolve with additional information rather than making the same static choices no matter the input.

Use of Machine Learning in Software Development

The following are some of the ways in which machine learning algorithms are used in software development. The list doesn’t cover every use, as there are more being developed all the time, but some of the more popular.

  • Neural Networks – The word “neural” in this phrase represents machine learning algorithms that simulate cognition, and the network is generally comprised several components (computers, servers) that each gather input for the dynamic “thinking” of a central body.
  • Data Mining is a multifaceted aspect of most data-driven applications. Machine learning comes into play for large data sets that are too big to expect that a human being could check their accuracy. In these instances, the machine must learn to implement its own models for consistent accuracy. Machine learning are used for results validation, classification, regression, and dependency modeling, which are all just complex ways of saying that algorithms find the patterns in large data sets and react dynamically.
  • Natural Language Processing (NLP) – Each time we speak into our phones and receive information in return, we are seeing the results of NLP. In this case the machine is learning to recognize patterns of speech that are divided into segments call tokens. It is impractical to try and teach a machine to understand language in a closed lab, since people have different dialects and inflections in different regions of the same country; thus, machine learning is used to gather data on dialects from everyone, everywhere, and form logical conclusions about what people are saying.

Practical Application of Machine Learning in the Real World

The following items present some of the ways machine learning algorithms are applied to everyday use. These applications may seem simple on the surface, but they are applying simulated reasoning that is the direct result of machine learning:

  • Web Searching – What began as a relatively straightforward indexing engine has grown into a search platform that has a mind of its own. We’ve seen how Google Now “remembers” previous searches and uses them to provide suggestions based on our interest, but it’s also applying weighted values based on a constantly evolving set of parameters. Behind the scenes, complex machine learning algorithms that are fed information daily from hundreds of millions of test cases. In this, Google Search is an example of an enormous neural network. Currently, popular web search engines like Google and Bing also use natural language processing as a means to understand spoken query and match it to tokens from an enormous repository of matching symbols. The combined efforts of neural networks and NLP are what makes modern search engines so dynamic.
  • Business Analytics are most effective when they have robust data mining capabilities backed by machine learning algorithms that are capable of recognizing patterns. Since business is a constantly evolving entity, static programming doesn’t have the agility to keep up with such a vast number of sources. Machine learning is necessary to separate that which is relevant to the user’s needs from the rest, and to present in a way that is digestible by people.
  • E-Commerce, particularly with enormous databases and user activity like Amazon.com, use machine learning to provide better service to consumers. Where we see these sites offering additional results under such banners as “people also search for”, the presented items are constantly changing based on the underlying algorithms which simulate the machine learning our shopping tendencies.

Future of Machine Learning Algorithms

To those outside of the software development industry, machine learning may sound like something from a science fiction movie. They may think of robots with personalities or machines that perform all our tasks for us. This isn’t too far from the truth, however. We’ve already given over many tasks to machines that would have been performed manually, say, ten years ago. Therefore, to those involved in software development, machine learning should be high on the list of skills to master, as contemporary thinking is all headed in the direction of pseudo-artificial intelligence. Meaning, it may not be long before some of those science fiction concepts become reality.