Machine learning (ML), or deep learning, depends on algorithms to inform what actions are taken and then produce an inferred function. In the future, we may see machines achieve true self-awareness and operate independently from human, data-influenced input. But for now, humans and data will continue to play a critical role in shaping machine-driven predictions.
There are two main methods to guide your machine learning model—supervised and unsupervised learning. Depending on what data is available and what question is asked, the algorithm will be trained to generate an outcome using one of these methods. The difference between them is that supervised learning uses a full set of labeled data during training. In unsupervised learning, the data set is provided without explicit instructions on what to do with it; the machine is basically winging it.
What is supervised learning?
The supervised learning technique is more commonly used in machine learning because it deals with straightforward tasks and is easy to implement. Data inputs are labeled with the answer that the algorithm should arrive at, which helps the machine pick out patterns in the future, better differentiate data, or make predictions.
Supervised learning is classified into two categories of algorithms and is ideal for problems where there are reference points available.
- Classification: A classification problem exists when the output variable is a specific category.
- Regression: A regression problem exists when the output variable is a real value that fluctuates (i.e. dollars, weight, measurement).
We regularly use supervised learning to teach ourselves or someone else a new task. It’s kind of like being given a test with the answer key. Once you have the task mastered, this technique can be applied to similar processes and information.
What is unsupervised learning?
In this technique, the machine learning model learns organically instead of receiving a data set with explicit instructions. It then tries to automatically find structure in the raw data through analysis and interpretation.
While supervised learning is easiest, we don’t always have access to complete, perfectly labeled data sets to train the algorithm. Where supervised learning has the “right” answer, unsupervised learning is helpful in situations where analysts (or really anyone) ask questions and the algorithm doesn’t have the answer, or there’s more than one answer. The unsupervised learning model is classified into four different categories of algorithms, which group data based on similarities or relationships among variables:
- Clustering: The deep learning model looks for data and features that are similar then groups them together.
- Association: By reviewing key attributes in the data, an unsupervised learning model can predict other attributes that they’re commonly associated with.
- Anomaly detection: In this instance, the model is used to call attention to data outliers. For instance, banks detect fraud by looking for unusual purchase patterns with customers—like if a card is used in two very different locations in one day, the bank notices and investigates the activity.
- Artificial neural networks (or autoencoders): An autoencoder takes input data, compresses it into code, and then tries to recreate the input from that code while removing any signal noise so data quality is improved.
Other, less-known machine learning methods
There are other, less common methods for machine learning that we’re starting to see used more frequently, perhaps because we live and work in a time-constrained and often reward-driven culture.
- Semi-supervised learning: This method combines aspects of supervised and unsupervised learning, where the process and reference data are known, along with the intended result—but the data is incomplete. It pulls from supervised learning using the reference data that’s available, but also incorporates unsupervised learning to make a best guess on the new result.
- Reinforcement learning: This method uses rewards and feedback to find the optimal method of accomplishing a task. For example, this is used to train robots with artificial intelligence (AI), and we experience it when playing video games that give rewards after completion of a task.
Learn more about machine learning and artificial intelligence
Practical applications of supervised and unsupervised machine learning are all around us. Learn about some common machine learning examples—some of which you may encounter every day, depending on your line of work.
Since there’s considerable noise and debate around machine learning, it’s also helpful to demystify some common machine learning misconceptions. This will help you understand how to harness the power of machine learning and embrace the opportunities it provides.
Thinking of implementing ML or AI in your organization, or just want to dive deeper? Follow these AI and ML industry blogs to learn about what’s hot in the market.