Most humans use machine learning systems daily without giving it a second thought. We have grown accustomed to using the smart technology that is continuously advancing, thanks to digital researchers, engineers and developers.
The term “machine learning” is increasingly used by many, but it is still an enigmatic concept to many people. This article intends on demystifying the world of machine learning in simple terms.
First of all, it’s important to lay out a clear definition of machine learning in computer science; Machine learning falls under the category of artificial intelligence (AI) and focuses on creating and using data-focused algorithms. These algorithms intend to continually learn from patterns in human behaviour and attempt to imitate them, improving with accuracy over time.
An example of machine learning is a ‘recommended songs’ section on a music streaming platform. The ‘recommendation’ algorithm compares different properties of songs you have listened to and other music which people with similar listening patterns have listened to. Based on factors such as the genre, artist and mood of each song, the algorithm decides whether or not to recommend it. Then, the statistics of whether the recommendation is received well or not is taken into account for every future decision.
How does Machine Learning work?
Secondly, let’s look at exactly how the machine learning algorithm works. We can describe the general process with these three main steps:
Decision
The decision process includes the algorithm using pre-conceived patterns in data to take a “guess” on what it’s supposed to produce.
Error
Next, the algorithm compares and measures the results by effectiveness. Was the decision produced a correct one? If not, how far from correct was it? This process is called an error function.
Optimization
Finally, the algorithm examines where it fell short and updates its decision process not to make the same mistakes again.
Methods of Machine Learning
These are the four main categories of machine learning methods:
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Supervised machine learning
With supervised learning, a labelled dataset is present to train an algorithm. The algorithm judges whether it’s coming up with correct results by comparing them to the given references.
An example of this is the automated “Spam” folder in your email box.
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Unsupervised machine learning
When labelled data is not available for a given objective, unsupervised learning comes into place. This method comes in handy when researchers don’t want to create an algorithm with specific instructions or desired answers to questions. The deep learning model runs free to find its own patterns and solutions from data without input from a user.
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Semi-supervised machine learning
As the name suggests, semi-supervised learning is a mix of both supervised and unsupervised machine learning. This method is suitable for taking out complicated tasks while also saving time for users.
An example of this kind of learning is medical scans such as X-rays or MRIs. A medical professional can examine and label a smaller subset of scans. Then, the algorithm will learn from the labelled and be able to make labelling decisions on future scans in a faster manner.
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Reinforcement machine learning
Reinforcement learning focuses on predicting step-by-step actions to achieve a final goal and ‘reward’. This method uses incentives to encourage the algorithm to find the most optimal solutions and routes to completing tasks.
Some examples of artificial intelligence that may use reinforcement learning are robots and self-steering cars.
Using Machine Learning for business
Web Scraper and Crawler bots
Targeted Ads & Recommendations
By analyzing human behaviour, machine learning can identify opportunities to show the right content to the right people at the right times. People can use this to their advantage by using paid adverts or through optimization methods such as SEO.
In general, machine learning can benefit many aspects of organizations through effective data management, identifying diagnoses, targeted marketing, and more.
Advanced algorithms are able to understand, learn, predict and adapt, getting smarter every time they make a decision. An endless amount of possibilities are in store for machine learning as researchers constantly elevate the capabilities of the technology.
Do you have an idea for something that uses digital learning? Let’s talk about it.
