Machine learning is the future of business culture. Machine learning is sci-fi. Machine learning is deeply ingrained in our everyday lives. All three statements hold true today, it just depends on the angle from which you’re eyeing this technology.
But there’s one thing that remains apparent no matter the angle: machine learning has redefined the way businesses handle data. Nowadays, it’s easier to meet user expectations by implementing ML-based algorithms like face recognition or NLP. With that said, let’s have a look at the two main species of ML: supervised and unsupervised learning.
The Fundamentals of Supervised Learning
Supervised ML involves having a complete set of labeled data sets to train the model at all stages of its building.
Having a tagged dataset means that each example in the training set corresponds to the answer that the algorithm is supposed to retrieve. Thus, a marked dataset of photos of animals will train a machine using a picture of rabbits, foxes, or wolves. When you add a new photo, the computer will compare it with the examples from the training dataset to predict the answer.
Supervised machine learning falls into two types of models if we’re talking about data mining. These include classification and regression:
- Classification applies an algorithm to recognize the object and categorize it into a particular group. In simple words, it may come down to labeling emails as “spam” or “not spam.”
- Regression implements an algorithm to define the relationship between response and input variables. Regression is further subdivided into various techniques that range from the simplest like linear to sophisticated models like neural networks.
Why Is Supervised ML Popular?
- Supervised learning allows you to eliminate manual classification work.
- It helps map new examples by analyzing the training data.
- It also aims to maximize efficiency using previous experience
- It can solve real challenges such as credit card fraud detection, face detection, and recognition.
Supervised Learning: Use Cases
Now that we’ve covered the basics, let’s have a look at classic supervised machine learning applications.
Marketing and Sales
Digital marketing and online-driven sales are among the most popular supervised machine learning applications. Supervised algorithms help businesses to deal with the following areas of interest:
- Lifetime Value – to forecast most budding clients early and channel target sales effort.
- Churn – to analyze reasons for churn customers and customize offers to decrease the churn rate.
- Sentiment analysis – aggregate and perform analytics on customer behavior, intent, and feedback.
- Recommendations – to identify the items that a particular customer is most likely to buy next and include them into the recommendation system.
Data-driven team management is also an essential element of a successful organization. Thanks to supervised ML, business owners can make decisions about their workers relying on deep analysis of data rather than the knee-jerk reflex or previous experience. Let’s dwell on this use case:
- Sales performance – to assess lead conversion, salespeople’s performance and predict what actions yield better results.
- Retention – to monitor job-hopping behavior among employees.
- Human resource allocation – to predict turnover periods or a labor shortage.
Unsupervised ML is widely used for detecting security issues or vulnerabilities. It is especially helpful for anomaly detection. Other use cases include:
- Spam filtering – to sort out this type of correspondence by using meta- and textual data.
- Malicious emails and links – to recognize a variety of content types that are dangerous to an organization.
- Fraud detection – to block potentially fraudulent actions.
Other real-life applications of supervised ML include asset maintenance and IoT and the entertainment field.
The Basics of Unsupervised Learning
Unsupervised ML is a type of learning that analyzes and clusters unlabeled data. Whereas in supervised learning the machine tries to learn from previous examples, in unsupervised learning the system tries to define the patterns directly from the given example on its own.
Let’s imagine a toddler. The child has seen a rabbit in the book and now knows what this animal looks like. However, the toddler doesn’t realize that rabbits have different fur colors and patterns. Yet, the child will be able to recognize a rabbit next time one sees it. The trick lies in a particular set of features that are unique to rabbits such as long ears, short fluffy tails, and strong, large legs.
If we shift to machine learning, this learning process would be called unsupervised learning.
This ML type cannot address the regression or classification problem since there is no upfront information about the values for the output data. Instead, you can use it to explore the underlying structure of the data.
Several algorithms implement unsupervised learning, but clustering has been the most widely used technique so far. Clustering, also known as segmentation, refers to identifying groups of objects with comparable featu from the original data set and is often the first step in data analysis. Clustering allows you to simplify the work with data for further processing.
Why use Unsupervised Machine Learning?
- It is indispensable for extracting valuable data insights.
- Unsupervised learning resembles the way humans think, which applies to Artificial Intelligence.
- This type of learning relies on unlabeled and uncategorized data.
- We don’t always obtain input data with the corresponding output, hence supervised learning can help in such cases.
Unsupervised Machine Learning: Use Cases
Unsupervised ML and clustering in particular have suffused across various industries from marketing to data science. Thus, machine learning examples include:
- Data exploration – to identify the relationships between variables in data sets, generate a hypothesis, and find sample groups that require more data points.
- Customer segmentation – to handle customer data and define recurring patterns across different features.
- Recommender systems – to predict the preferences of customers
- Target marketing campaigns – to focus tailored marketing efforts towards a particular group of customers, etc.
This is not an excessive list of all unsupervised ML applications. Its ability to identify patterns in unclassified and unlabeled data points is broadly leveraged by most industries to gain a competitive advantage and automate manual labor.
How Are Both Different?
Supervised and unsupervised learning both belong to machine learning. The key distinction between the two lies in the nature of data they’re putting to use. Thus, supervised learning requires labeled data, whereas unsupervised learning uses unlabeled data. We’ve shortlisted other differences in the table below:
|Supervised ML||Unsupervised ML|
|Concept||learning a function that maps inputs to outputs based on examples of input-output pairs||inferring a function to describe a hidden structure from unlabeled data|
|Basic functionality||predicts the result based on labeled input data||predicts the result without labeled data by defining the patterns|
|Accuracy||More accurate and reliable||Less accurate|
|Core algorithms||RegressionClassification||ClusteringDimensionality reduction|
The Bottom Line
Machine Learning has added a new wrinkle to analyzing vast volumes of data. Although it’s not a completely new science, it has reached a new milestone with lots of businesses embracing ML to reset their perspectives. Both unsupervised and supervised types of ML help us make data-driven recommendations and decisions based on only the input data.
Machine learning has applications in all types of industries from manufacturing to feedstock and is yet to embrace its full potential in the coming years.