What is machine learning and how does it work? This article explains everything you need to know about it.
From public safety, website ad recommendation to fraud detection, machine learning powers computers to engage in activities that were in the past, only done by people.
It brought the endless capabilities of Artificial Intelligence, a field of study that allows machines or computer programs that think and act like humans. So, what is machine learning and how does it work? Let’s find out in this article.
What is Machine Learning?
Machine learning (ML)is a method that teaches computers to make accurate predictions to outcomes without any explicit instructions to do so.
It’s a branch of Artificial Intelligence, based on a concept that computers can learn from data, predict patterns, and make decisions with minimal human instructions.
These predictions can identify whether an email is a spam, spot a cat in a photo, and accurately recognize speech patterns.
In old computer software, a programmer hasn’t programmed a system that identifies the differences in let’s say, between legitimate and spam emails.
Instead, a machine learning model can be taught to filter emails by training it on a huge amount of data. In this case, this data is most likely a huge collection of emails labeled as either spam or otherwise. In other words, exposure to a huge amount of data is what makes ML possible.
How Machine Learning Evolved
Due to the advances in technology, machine learning today makes huge strides from its predecessors. It came about from a theory that you can teach computers to execute certain tasks without programming them.
Additionally, its repetitive nature is crucial. Exposing ML models to new data allows it to adapt accordingly.
These models learn from past computations to generate repeatable and accurate decisions and outcomes. While the science behind is not original, it has achieved quite a momentum over the years.
What are the uses of Machine Learning
Whether we are aware of it or not, most industries today use machine learning in all sorts of applications. A popular example is the recommendation algorithm that powers YouTube feed.
YouTube is increasingly effective at identifying the types of clips that a user is interested in. It has evolved from site driven by search and search terms. YouTube has undergone redesigns and thousands of experiments in the field of AI.
Today, The YouTube home page opens to videos from subscribed channels and other videos customized based on interest. They have created a system that not only makes their recommendations personalized but also accurate.
Aside from the recommendation algorithm, technology companies use machine learning in:
- Human Resource Information System – it utilizes the machine learning model to filter job applications and the right candidates for specific positions.
- Image Recognition – It applies to face detection in images and character recognition to distinguish printed and handwritten letters.
- Virtual Personal Assistants – Alexa, Siri, and Cortana are a few examples of virtual personal assistants. They use it to collect and refine the data based on your previous commands (e.g. asking today’s schedule or weather, setting an alarm, etc.)
- Customer Relationship Management – CRM tools adopt machine learning to collect and analyze leads. It prompts sales teams to prioritize important leads first.
- Traffic Management – It can help in estimating which areas with the most traffic congestion based on daily traffic experiences.
- Financial Services – By using machine learning, banks and other financial institutions can make sound financial decisions. They can spot when a client is about to close their account or track their credit card spending patterns.
Types of Machine Learning
There are two most common approaches. These are supervised learning and unsupervised learning.
The type of algorithm that a data scientist use depends on the type of data they want to predict.
In this approach, supervised learning trains machines through labeled examples. It maps each input object to the desired output.
For example, systems can be fed with a vast number of handwritten images annotated to classify which numbers they correspond to. With numerous data given, a supervised learning system can study the shapes and clusters associated with each number and accurately identify handwritten numbers and differentiate one number from another.
But, training a supervised learning algorithm needs a huge amount of data; some systems may need exposure to millions of examples to be an expert in a task.
On the other hand, unsupervised learning systems are used on data without historical labels. It isn’t trained for the right answers and figures out based on what is shown.
Unsupervised learning aims to analyze data and figure out the structure within. Unsupervised learning identifies data patterns and pinpoints similarities that divide the data into categories.
As an example, Google News aggregates its daily news stories based on similar topics. In marketing, an unsupervised learning system can group customers with related attributes or look for the main attribute that differentiates customer segments from each other.
In essence, its algorithm does not to isolate a particular type of data, but to look for data that can be clustered into similarities or anomalies.
As the name suggests, semi-supervised learning is a hybrid of supervised and unsupervised learning.
The algorithm relies on the small amount of labeled data and a huge amount of unlabeled data for training.
It uses the labeled data to partially train a machine learning system. Then the partially-trained system labels the unlabeled data. This is called pseudo-labeling. The system then trains on the combination of labeled and pseudo-labeled data.
In reinforced learning, the system uncovers through trial and error which actions lead to the best outcome. It is common on gaming, robotics, and navigation.
Think of the first time you played a computer game. You don’t know the rules or how to control the game. As you play the game regularly, you eventually play the game better.
Reinforced learning includes three primary agents, namely:
- Agent – the decision-maker or learner
- Actions – the actions that the agent does
- Environment – anything that the agent engages with
The end goal here is to choose actions that maximize the potential of an expected reward in a specific amount of time. The agent will get to his or her goal faster if they follow a good procedure. Reinforced learning is all about learning the best procedure to arrive at the end goal.