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5 Important Topics in Machine Learning You Need to Know

Published: March 7, 2022
Editor at Plat.AI
Editor: Ani Mosinyan
Reviewer at Plat.AI
Reviewer: Alek Kotolyan

Machine learning is a tool that incorporates practices from different fields and aims to build generalizable models to predict outcomes. Under the umbrella of artificial intelligence, machine learning leverages already existing algorithms to extract new patterns from data. 

Many artificial intelligence topics are points of discussion now, but machine learning is something we can already see in practice. From banking institutions to telecommunications, many industries are increasingly employing machine learning algorithms to improve operations. 

This article will focus on five essential topics in artificial intelligence and machine learning, seeing how they work and where they’re used. 

Machine Learning 

Machine learning.

Machine learning is being utilized by businesses to extract more meaningful data. Insight and patterns from data allow companies to implement cost-effective and competitive strategies quickly, ultimately increasing revenue, customer retention, and consumer satisfaction. Here are some of the top machine learning topics and how they can help your business succeed:

Supervised Learning

A subcategory of machine learning and artificial intelligence supervised learning is programmed to learn from labeled data. Labeled data comes with tags like text or numbers, whereas unlabeled data has no tags. For example, a photo of a dog with the label “dog” or “14 lbs” is an example of labeled data, whereas an image of a dog that doesn’t have any label is an example of unlabeled data. 

During training, the inputs to the model are paired with correct outputs, so the algorithm knows what to search for. After the training phase, it starts looking for patterns in the data that correlate with desired outputs. It works like a function at its core, classifying the inputted data into new labels and categories. 

This machine learning area aids organizations in solving a variety of problems, such as customer churn, employee attrition, product sales price forecasts, and classifying spam. For example, Pinterest uses supervised learning to moderate spam and content discovery, as well as to reduce the number of churns of email newsletter subscribers. Supervised learning is also used in bioinformatics to preserve human fingerprints and iris texture that are later implemented into mobile phones to increase security. 

Unsupervised Learning

This machine learning topic infers a hidden structure, meaning the pattern operates on unlabeled data. Unlike supervised learning, unsupervised learning operates without prior training. Put simply; it is left alone to search for new patterns and make predictions in the data through predictive analytics software. One of the primary purposes of unsupervised learning is to group similar pieces of data together through clustering.

You’ll see unsupervised learning used in market analysis, customer and image segmentation, delivery store optimization, and identifying areas that are prone to accidents. For example, YouTube uses this module to categorize your channel’s subscribers into different categories like regions, countries, or genders. Many organizations also use unsupervised learning to detect anomalies, such as fraud.

Semi-Supervised Learning

Semi-supervised learning takes up the middle ground between supervised and unsupervised learning techniques. These machine learning modules use both labeled and unlabeled data, the latter being predominant. The goal of semi-supervised learning is to predict and classify unlabeled data using the labeled information set. 

Semi-supervised learning models are commonly used in industries that still require human involvement. For example, semi-supervised is applied in speech analysis to label audio files, a task that still requires human intervention. It is also used in web content classification to organize billions of web pages on the Internet. 

Yelp leverages semi-supervised learning to deliver a top-notch user experience by improving its picture classification technology, namely categorizing and labeling photos more efficiently. 

Reinforcement Learning

Learning with AI.

Like the word reinforcement, knowledge is amassed through trial and error. With reinforced learning, the agent learns how to behave in an environment by performing specific actions and observing the results. The algorithm evaluates the situation and performs an action for which it receives either positive or negative feedback. Think of this module as learning how to ride a bicycle, you may not know what the correct movements are when starting, but with enough practice, you master the ways of a smooth stroll.

Reinforcement learning is utilized to optimize operational productivity in robotics, manufacturing, academics, and supply chain logistics. For example, UK company Wayve has developed self-driving cars using reinforcement learning. As a result, reinforcement learning modules assist in parking, lane changing, and controller optimization. Ad recommendation systems are another industry that employs reinforcement learning to help personalize ads for specific end users. 

Neural Network or Artificial Neural Network (ANN) 

The Artificial Neural Network (ANN) model is based on how the human brain functions. The model is designed to mathematically model the biology of a brain and mimic its tasks. ANN can recognize objects, speech, and animals similar to brain cells called neurons. 

Neural networks are applied in facial recognition, stock market predictions, and social media, to name a few. Netflix is probably one of the most prominent companies that employ artificial neural networks to deliver outstanding customer experience. These models allow Netflix to tailor customized recommendations and predict what shows users would be interested in.  

Bottom Line

To conclude, whether you plan to dive into artificial intelligence or not, these machine learning topics surround us everywhere we go. From supervised learning to artificial neural networks, companies rely heavily on these tools to maximize their productivity and stay ahead of the competition.


Tigran Hovsepyan

Tigran Hovsepyan

Staff WriterTigran Hovsepyan is an experienced content writer with a background in Business and Economics. He focuses on IT management, finance, and e-commerce. He also enjoys writing about current trends in music and pop culture.


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