Top Applications of Machine Learning
Overview
Machine learning is one of the most demanding skills in the technology world right now. If you are someone who follows technology news closely, you must have noticed that machine learning is getting used in numerous spheres of our society. In this article, we will learn about some of the common yet impactful applications of machine learning in human civilization.
Prerequisites
- You must know the definition of machine learning.
- You must have a basic understanding of different types of machine learning paradigms i.e. supervised, unsupervised, semi-supervised, and reinforcement learning.
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As mentioned in previous articles, machine learning is all about making computers capable enough to learn from data and make more accurate decisions as it becomes experienced. Now, this might appear very trivial to someone, and they may wonder why there is such a buzz about machine learning everywhere. To answer this, here we will pen down some of the path-breaking applications of machine learning in our day-to-day life.
Applications of Supervised Machine Learning
Image Recognition
One of the hottest applications of machine learning is image recognition. From identifying fake accounts to automatically tagging friends in social media, image recognition makes our social media experience smoother. Image recognition is used extensively by government agencies, e-commerce websites, and in reverse image searches.
Speech Recognition
Another promising application of machine learning is automatically recognizing speech. For example, generating movie subtitles, controlling home appliances, call routing, etc, are some of the very common applications of speech recognition, making our life easier and more comfortable.
Traffic Prediction
Google uses machine learning to predict traffic on a route which helps us to make better decisions before leaving for our workplace.
Product Recommendations
Product recommendation powered with machine learning capability helps e-commerce companies to target specific customers with the correct set of products.
Self-driving Cars
Self-driving cars are one of the most interesting applications of machine learning in today’s world. These cars use different sensors and cameras to collect data from the surroundings, apply machine learning to interpret the data, and make judicious decisions.
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Our inbox is not overloaded with spam emails, thanks to the machine learning algorithms which can automatically detect and dump them in the spam folder.
Virtual Personal Assistant
Apple’s Siri and amazon’s Alexa are some examples of virtual personal assistants, which we all use as one of the fanciest machine learning applications.
Online Fraud Detection
Every time a transaction is made, a machine learning algorithm monitors if there are any suspicious patterns in the transaction. Thus it is solving a problem that costs more than a few billion dollars each year to credit card users.
Stock Market trading
Trading tools built using machine learning are helping traders to make informed decisions for maximizing profit without taking extra risk. This is one of the most impactful applications of machine learning in the financial domain.
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Medical Diagnosis
Machine learning has revolutionized medical diagnosis to a great extent. Machine learning tools built using Counterfactual and causal analysis (a branch of machine learning) are working as a companion for medical professionals to detect patients’ symptoms.
Automatic Language Translation
Language translation tools are helping to truly democratize the internet. People from remote areas can now use their native language to learn new skills, which was impossible a few years ago.
Application of Unsupervised Learning
Unsupervised learning is another paradigm of machine learning (already covered in previous notes) with important real-life applications, including clustering, anomaly detection (identifying potential loan defaulters), recommendation systems, etc. For example, clustering similar data pieces into clusters helps unfold various business insights no one ever knew. Anomaly detection is all about identifying outliers in data which is again a very impactful application of machine learning. It is also used to discover relationships and associations between different variables in large-scale datasets, which is useful in building recommendation systems.
Application of Semi-supervised Learning
Semi-supervised machine learning is useful in applications with scarce and costly labeled data. It is helping to produce improved results for speech and text analysis, image classification, etc. For example, against each query, google uses a semi-supervised algorithm to rank a webpage. Semi-supervised learning has also contributed to the application of protein sequence classification, which would have required active human intervention in the absence of this application.
Application of Reinforcement Learning
Machine learning practitioners use reinforcement learning in robotics and build strategies for online gaming applications. Many tasks are complex for humans, but a robot can do them easily, thanks to the framework and set of tools that reinforcement learning offers. Robots worldwide are helping to reduce manual efforts required for different activities. Gaming agents are outperforming human experts in different games by taking certain actions that will produce the highest reward.
Conclusion
- Machine learning has had such a profound impact on our society.
- Here, we have discussed some of the important applications of machine learning in different domains.
- Researchers worldwide think it’s just the beginning, and there are many problems where machine learning can disrupt the industry. We will come across many such problems while reading subsequent articles.
