Machines are now learning. That’s a fact. The more our world becomes data-directed, data-reliant and data-driven, the more we need a helping hand in order to control it. Even if its a hand of a robot. Or an algorithm.
In order to utilize this tool more efficiently, let’s look into what is a Machine Learning model, meet the most recent Machine Learning definition, and review Machine Learning examples in our new article.
What is Machine Learning?
Before learning the direct Machine Learning definition, let’s look at its origin a little bit closer. The term “Machine Learning” first appeared in 1959 and was created by an American computer gaming and artificial intelligence pioneer, Arthur Samuel.
The first Machine Learning definition though was proposed by another computer scientist and professor – Tom M. Mitchell, and it sounds something like this: “A computer program is said to learn from experience (E) with respect to some class of tasks (T) and performance measure (P) if its performance at tasks in (T), as measured by (P), improves with experience (E).”
In a much simpler, present-day definition Machine Learning is an algorithm that can learn from data and act according to this knowledge without extensive prior programming.
The Machine Learning model is a part of Artificial Intelligence (AI) and creates computer programs that not only learn from presented data and improve themselves without any human interference, but can also make accurate predictions and are often called “predictive analytics” platforms.
If you love data, here’s another topic you will enjoy exploring – What is data mining? Definition, types and data mining solutions
Machine Learning models
The prime goal of a Machine Learning algorithm is to generalize from its experience and then use the acquired knowledge in order to solve new, previously unknown and unseen tasks. Just like humans, actually.
There are 9 (!) different types of Machine Learning algorithms, each serving a different purpose and using distinct tools, let’s take a look at them:
- Supervised learning – here Machine Learning algorithm is presented with both – input and the desired output data before it starts learning, the same human procedure we call “process learning”.
- Unsupervised learning – as opposed to the previous type, this Machine Learning algorithm is only presented with input data and has to structure data on its own.
- Semi-supervised learning – a Machine Learning method that uses combined parts of data from Supervised learning and parts from Unsupervised learning types, said to produce heightened learning accuracy quality.
- Reinforcement learning – is the Machine Learning type you are familiar with quite a lot (if you like videogames that is), as it teaches a computer algorithm how to play as a human opponent.
- Self learning – just as it sounds, it is a Machine Learning type that uses no external rewards and no external teacher advises.
- Feature learning – replaces manual feature engineering and enables a Machine Learning algorithm to both learn the features and utilize them to perform a specific task.
- Sparse dictionary learning – a Machine Learning method whose goal is to find a sparse representation of the input data in the form of a linear combination of basic elements, which are called atoms and compose a dictionary.
- Anomaly detection – recognition of untypical events, items, or bursts in activity that are significantly different from the majority of the data.
- Association rules – a rule-based Machine Learning method for identifying relations and strong rules among variables in large databases.
After you’ve had a quick tour over the 9 Machine Learning types or methods, you must be dying to see some of the real-life Machine Learning applications. You’re in luck 😉
Machine Learning examples
Machine Learning and Artificial Intelligence applications are implemented among a vast number of business models we have no idea about. So let’s look into what is considered a good Machine Learning example.
The all-famous American Express performs transactions with trillions of US dollars having over a hundred million cards in operation. Taking into account these numbers makes it easy to understand why using Machine Learning algorithms is crucial in order to detect fraud in real-time, helping the company to save millions in losses.
Staggering pollution in China we’ve all heard about gave rise to a new Machine Learning algorithm that scans CT scans for signs of an early lung cancer. Infervision, a Beijing-based high-tech company, uses image recognition and deep learning to mimic the work of radiologists and assist in identifying abnormalities much faster and with drastically fewer errors.
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And of course the robot-written stories. Yes, you read that right. A British news company Press Association and a news automation platform Urbs Media collaborated on a new project called RADAR, which produces over 30,000 news stories every month.
Using data from public authorities, government services and other regional institutions combined with natural language generation allows for the creation of up-to-the-minute articles that wouldn’t have been possible otherwise.
Conclusion
With the ever-growing number of impressive Machine Learning examples, it’s hard to argue over the effectiveness of the tool. And we shouldn’t. It is not only an inevitable part of the far future, but also a present-day reality of hundreds of companies around the world.
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