Machine Learning: An Introduction

Our ability to learn and get better at tasks through experience is a fundamental part of being human. Just think about how we all come into the world, as babies we know very little, and we learn at an incredible rate based on the world around us. The goal of artificial intelligence (AI) is to create machines that can mimic the human mind. Until now, whereas humans learn from experiences, computers have learned from instructions. However, to allow machines to imitate humans, although they need learning capabilities, they also need knowledge representation and abstract reasoning. They need to be able to teach themselves by recognizing patterns, making predictions and learning from experience.

What Is Machine Learning?

The term machine learning is often used interchangeably with AI but, although it’s connected,  it’s actually a distinct area of computer science. Machine learning is a subset of AI that involves the writing of software that can learn from past experiences. These experiences, in the world of computers, come in the form of data.Machine learning is actually more closely related to data mining and statistics than AI. The technology allows computers to learn without being specifically programmed with predefined rules. Computers are able to improve how they perform based on past experiences, and in this way, they learn. Instead of programmers giving the solutions, with machine learning computers seek statistical patterns within data to work out the answers to questions independently. It is ultimately the extraction of knowledge from data based on algorithms formed from repetition and prediction.

What Are The Main Types Of Machine Learning?

Although, in its simplest form, machine learning is the process by which a machine recognizes patterns, makes predictions and learns, there is a little more to it. There are three principal ways in which a machine can learn:

  1. Supervised learning – computers are given pre-labelled training data with defined characteristics. The process is repeated multiple times until the system is able to learn and accurately provide the correct output. Supervised learning is either in the form of regression, where numbers are grouped to make predictions or classification, where images are identified based on binary numbers.

  2. Unsupervised learning – computers learn to classify objects without being given any prior information. The concept is that when machines are exposed to large volumes of varied data, they will be able to learn from it. With enough data, systems are able to reduce excessive variables using dimension reduction, identify key differences, and cluster data.

  3. Reinforcement learning – computers are trained using a system of rewards and punishments to validate the algorithms and outcomes they produce. Over time the cumulative effect of reinforcement allows the systems to make decisions to increase the rewards using dynamic programming. This method is the most advanced as it allows machines to learn without the programmer telling them how to carry out the task.

These different types of machine learning can be used for various functions. Supervised learning is ideal for speech and visual data prediction. Unsupervised learning is best used for random collections of data. Reinforcement learning can be used for creating physical robots. However, it is often the case that these different types of learning are combined for various applications focusing on detection and prediction.

What Can Be Achieved With Machine Learning?

The predictive ability along with the capacity to handle enormous volumes of data allows machine learning to process complex situations efficiently and accurately. The implications for business are far-reaching and, as machine learning can be used to do pretty much anything, it has the power to disrupt every industry. It’s no surprise that a roundup of machine learning forecasts and market estimates has found that 61% of organizations picked machine learning as their company’s most significant data initiative for next year. Machine learning is already widely used for the likes of facial recognition, text and speech recognition, spam filters on email inboxes, online shopping recommendations, and credit card fraud detection. It is technology that we are all using, often without our knowledge. One of the obvious examples is the use of Google’s search engine. Google’s mission is to organize the world’s information and make it universally accessible and useful, and it does this with machine learning. Machines are able to conduct voice recognition, natural language processing and translation with an extremely low error rate. Machine learning already forms a vital part of many industries and the potential for the future is vast. From the possibility to diagnose serious medical conditions to the rise of robots, the opportunities are endless. The technology’s potential to provide us with hidden insights and predict outcomes offers amazing opportunities for businesses everywhere.

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About the author

Michael Ridland is the Co-CEO and Founder of Xam Consulting.

Design-led problem solving delivering digital solutions.

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