July 20, 2017 4:17 pm

What is machine learning?

Machine learning, like predictive analytics, refers to a set of techniques that are particularly well suited to solving classification and prediction problems. That is, it detects and exploits patterns in large amounts of data to help classify occurrences or predict future outcomes.

It differs from conventional predictive analytics more in origin, culture, and convention than in fundamental theory or goals and the two worlds are gradually merging into something more powerful and useful.

It is also different from data mining, which is aimed at discovering patterns in data that humans can understand. Instead, machine learning bypasses the need for human comprehension of data, allowing computers to respond to patterns so complex that it exceeds human grasp.

Indeed, the most salient aspect of machine learning techniques is perhaps the fact that they tend to be entirely data-driven. There are no assumptions about the structure of the data required, while conventional statistical techniques generally make strong assumptions about the underlying nature of the system. This means it can detect and exploit unexpected, subtle, and profoundly complex relationships among variables.

The price machine learning pays for this powerful feature is its need for vast amounts of data. Fortunately, in most environments, data is not a scarce resource anymore!

Where can machine learning be used?

If you have a large amount of data and it contains variables that you want to predict, classify, or segment then machine learning is for you. It is not picky about the data it uses. Beyond structured data in a relational database, there are powerful techniques for dealing with images, video, speech, and free-form text! Below are examples of various solutions.

Financial trading

Machine learning algorithms are capable of predicting and executing trades at superhuman high speed and volume. Traders can’t possibly predict what the stock markets will do when it comes to vast quantities of data.

Healthcare

Machine learning algorithms can identify variables to predict diabetes, heart disease, cancer, and other diseases in its early stages by using various physiological measurements. It does this by processing more information and spotting more complex patterns than any human is capable of doing.

Fraud detection

Machine learning algorithms can spot potential cases of fraud across many industries and fields. Banks and online payment systems are using such technologies to distinguish between legitimate and fraudulent transactions as well as fighting money laundering.

Recommendations

Online stores are using machine learning algorithms to analyse activity and compare it to the millions of other users to identify what they will possibly want to buy based on their search history, life style, interests, and other parameters. Such algorithms improve with time and can recognize possible purchases for a user’s family members and even friends.

Marketing personalization

Online stores are using machine learning to understand and serve their customers better as well as sell more to them. They capture all users’ activity and keep records of that. It allows them to target advertising for similar items through different platforms to the same users even if they didn’t purchase anything before but just searched for those items once.

Natural language processing (NLP)

Cognitive computing can be used in any industry with customer service support. Appropriate algorithms transforms a customer’s speech into text and present appropriate responses to the customer’s request. Conversational interfaces or chat bots, and virtual assistants like Siri and Alexa are already becoming part of our everyday life.

Online search

Another good example of machine learning applications is the increasingly intelligent web search engines available from Google and others. Every time user execute search machine learning algorithms watch how user respond to the results. If a user accepts the top results, then the engine assumes that the search was a success. If the user tries many other results then the program learns and provide better results in the future.

How can machine learning help you?

Data is rapidly becoming the central resource in domains as diverse as financial services and healthcare. Machine learning allows you to unlock the potential of those resources. If your data is overwhelming you, instead of supporting understanding, planning, and decision making then machine learning can help.

The best part is you don’t need to understand your data or machine learning. We’ll do it for you.