Introduction
Machine learning is a branch of artificial intelligence that allows computers to learn without being explicitly programmed. It’s been around since at least the 1950s, but it only recently gained mainstream attention thanks to big data and increased computing power. Nowadays, machine learning algorithms are used everywhere from social media sites like Facebook and Google to medical imaging. But what can non-programmers do with them? In this article we’ll go over four applications of machine learning that don’t require any coding skills: fraud detection, image recognition, natural language processing (NLP) and recommendation engines
Fraud Detection
Fraud detection is a form of data mining. It’s also a subset of machine learning, which means that it uses algorithms to predict fraud. Fraud detection models are used to detect fraud in credit card transactions, social security numbers and other personal information.
It’s important to note that there are many different types of machine learning applications that use similar techniques but have different purposes (like face recognition or driving cars).
You can think about this like you would any other software program–the goal is for it to learn from past experience so it can predict future events with greater accuracy than humans alone ever could achieve on their own!
Image Recognition
At its core, image recognition is the ability to identify objects in images. Image search, image tagging and even self-driving cars all rely on this form of machine learning as a key component of their functionality. However, image recognition can be used for more than just these applications: augmented reality (AR) also relies on this technology to superimpose virtual objects onto real-world scenes.
In this section we’ll cover how you can use Python to build your own AR application with OpenCV and TensorFlow!
Natural Language Processing
Natural Language Processing (NLP) is the study of language, and it’s used for many applications. NLP is a subset of machine learning, which is itself a subset of artificial intelligence. Many applications rely on NLP, including speech recognition and translation.
NLP can be used to analyze text documents or spoken language. For example, Siri uses NLP to understand what you’re asking it to do (like call someone) before sending your request over Bluetooth or Wi-Fi to your iPhone’s processor so that you don’t have to say “Call John Doe” every time you want something done by Siri! Similarly, Alexa uses natural language processing technology so that she can understand what her users are saying–and respond appropriately!
Recommendation Engines
Recommendation engines are software that recommends products, services, or content to consumers. This can be done in many different ways:
- E-commerce websites use them to suggest items based on what you’ve already bought. For example, Amazon might recommend books that are similar to the ones you’ve purchased in the past.
- Social media sites use them so that when you go on Facebook and see posts from your friends or news articles, they will be personalized based on your interests and preferences (which they know because of all the data they collect about us).
- Mobile apps often have recommendation engines built into them as well – think of how many apps ask for access to your contacts list when you first install it! This helps ensure that each person sees only relevant information about themselves rather than having everything dumped into one giant feed where it gets lost among everything else going on at once (and makes it easier for developers too).
Machine Learning applications can be easy to use.
Machine learning is a subset of artificial intelligence and can be used in many industries. It is often used to make predictions, classify data and perform other tasks that are difficult or impossible for humans to do.
Machine learning applications can be used by non-programmers because they have been designed with simplicity in mind. This makes them easy to understand and use for anyone who has some programming experience or just wants an introduction into machine learning techniques without having to learn how to code themselves!
Conclusion
There are many more applications of machine learning, but these are just some of the most common ones. Of course, there are many other types of data and problems that can benefit from machine learning as well. For example, voice recognition systems use ML to understand what we say to them and respond appropriately (or not). Another example is recommendation engines like those used on Amazon or Netflix which help us find new products based on what we’ve already bought before–this too uses ML techniques but with a different type of data input than what we saw earlier in this post!
