Machine Learning

What you need to know about machine learning, its importance and how it works

The world is changing in more ways than we can keep up with. In this changing world, it’s hard to ignore the influence of artificial intelligence (AI) and machine learning.

Although it’s nothing new (the famous Turing Test was developed in 1950), AI is now all the rage. Since being adopted as a field of research in 1956, AI has grown in leaps and bounds and today, you’ll hardly swing your arm without knocking out a few intelligent machines.

A huge part of the stunning progress comes from machine learning. For instance, in 2016, for the first time ever, a machine beat the world’s best “Go” player because of machine learning.

The implications of this progress are huge. In order to understand them, you need to understand these concepts and how they work.

What is machine learning (ML)?

First off, it’s important to correct the misconception that ML and AI are the same. No, they’re not the same and; no, they’re not similar enough that the differences don’t matter.

AI is basically the simulation of human intelligence in computers. It refers to any set of approaches or techniques, ranging from the pretty simple to the incredibly complex, that is employed to get a computer to mimic human behavior. If you create a set of simple rules that never allow a computer to lose at rock-paper-scissors, then you’ve created some basic AI.

Machine learning, though, is a subset of AI. It is one of the approaches used to achieve AI. With ML however, the focus is on teaching the computer to learn for itself how to complete a task.

The whole point of ML is to teach a computer the rules for certain tasks like playing “Go” and then allow it develop in such a way that it can adapt to changes in the task. This means, while playing “Go”, for instance, the computer can learn the rules for “Go” and then respond to a limitless number of moves, even those it wasn’t taught.

This is spades different from how computing and statistical analysis was done. The emphasis used to be on rule-based decision making where the computer was given a set of rules that would guide every decision it makes.

Of course, the computer would usually be correct but it couldn’t handle data outside the rules provided to it and more crucially, it couldn’t adapt to changes in the data simply because there were no rules for it. So, what you had was Garbage in Garbage Out (GIGO).

Now, thanks to machine learning, we have machines that can learn and “think” for themselves. And they’re getting better at it every day. According to AI experts, machines will be able to accomplish any intellectual task that humans can perform by 2050.

Why is it important?

Humans are pretty smart. But the problem is we just don’t see well enough. There’s a lot we want to know but most of the information we need is hidden in tons of dense data that will take us hundreds of years to sift through and hundreds more to make sense of, using conventional techniques.

Machine learning helps us take what we see and teach a computer how to see it as well. Then we feed it all the other stuff that we don’t exactly know what to do with, but which may be useful, and the computer comes back with more patterns and connections than we ever could.

The result is data (or information) that literally blows our minds. It can provide almost superhuman insight into entire fields of human endeavor. Insight that can help us revolutionize how we see and, eventually, do things.

That insight is already making a difference in several industries. In the financial services sector, machine learning is already being used to analyze vast amounts of data for the purpose of risk analytics, fraud detection, and portfolio management. In travel, with GPS traffic predictions. It’s also what you see in use with Amazon and Netflix recommendations.

We encounter uses of machine learning every day, and it is prevalence will only continue to grow. Companies that effectively execute machine learning and other AI technologies gain a massive competitive advantage. Businesses that fail to do the same will be unable to compete with those who embrace the new frontier. The impact that ML has on our lives will get so big that it is predicted to create more than $50 trillion of value by the year 2025.

How does machine learning work?

Machine learning operates on the basis of precise algorithms that enable computers to find precise rules for optimal behavior and then adapt to change. Machine learning algorithms use computational techniques to “learn” information directly from data without relying on a predetermined equation as a model.

The algorithms adaptively enhance their performance as the number of samples available for learning increases and they find natural patterns within the data, gain insights and predict the unknown for better decisions. Although many of these algorithms have been around for a while, advances in computer science and parallel computing have allowed them to be used in novel ways.

There are many techniques involved in machine learning, such as deep learning, support vector machines, decision trees, k-means clustering and a host of others. These techniques fall into two categories: supervised learning and unsupervised learning.

Supervised Learning Algorithms

Supervised learning algorithms are taught using labeled examples, such as an input where the desired output is known. Take, for instance, a piece of equipment with data points labeled either “F” (failed) or “R” (runs). The learning algorithm gets a set of inputs along with the corresponding correct outputs, and the algorithm learns by examining its actual output with the right outputs to detect errors. Then it modifies the model accordingly.

By using systems like classification, regression, prediction, and gradient boosting, supervised learning utilizes patterns to predict the values of the label on other unlabeled data. Supervised learning is typically used in applications where historical data predict likely future results. Such as it can anticipate which insurance customer is expected to file a claim or when fraudulent credit card transactions are likely to occur.

Unsupervised Learning Algorithms

On the flip side unsupervised learning is used with data that has no historical labels. This means the system is not given the “right answer.” The primary objective is to explore the data and find some structure within it. Unsupervised learning works well on transactional data. It can identify sections of customers with similar attributes who can then be treated similarly in marketing campaigns, for example.

Or it can find the main characteristics that separate customer populations from one another. The most well-known techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering, and singular value decomposition. These types algorithms are also used to segment text topics, recommend items, and identify data outliers.

Machine learning and RapidMiner

As you must have figured out by now, machine learning involves a huge amount of data. The process of feeding in the data itself is historically tedious and requires a lot of manual coding. This limits the ability of organizations to effectively utilize the advantages of ML. Causing delays that can make models outdated even before they are complete.

And it doesn’t end with just having to feed huge amounts of data into some ML model. In order to get the most value from ML, you need to pair the best algorithms with just the right tools and processes. That’s when the whole thing comes together.

RapidMiner provides a complete solution on a unified platform that supports the entire machine learning workflow from data preparation through model deployment to ongoing model management. The quick-to-learn and easy-to-use workflow designer accelerates end-to-end data science for improved productivity. With the cutting-edge tools and innovative solutions that RapidMiner provides, insights can now be delivered on a scale and speed greater than was ever possible.