What are Some of the Common Applications of Machine Learning?
Machine learning is everywhere. You may hear your friends mention it, read about it online, or just hear the term being tossed around at work. But do you actually know what machine learning is all about? In a nutshell, machine learning is a subset of artificial intelligence that allows systems to learn from data and improve on their own without explicit programming. It enables computers to learn from experiences as humans would.
If you want to dive into this field, taking a machine learning course would be a perfect way to do so! And who wouldn’t want to get into this lucrative field with such a high machine learning engineer salary? But before jumping into that, let’s take a glimpse at some of the common applications of machine learning.
6 Common Applications of Machine Learning
Here are some of the most common applications of machine learning in our day-to-day lives:
1. Recommendation Systems
Ever noticed how Netflix just magically seems to know what movie you might want to see next? Or when Amazon suggests products that just magically align with your shopping list? Well, that’s machine learning in action!
Recommendation systems make use of algorithms to gather information from your previous behavior and preference lists to make recommendations on items or content you are likely to be interested in. These systems are in common use in:
| Platform | Machine Learning Use Case |
| Netflix | Recommending TV shows and movies |
| Spotify | Curating personalized playlists |
| Amazon | Suggesting products based on your shopping habits |
| YouTube | Offering video suggestions based on viewing history |
ML collects tons of data regarding user interaction and preference and then predicts their future behavior. Recommendation engines have indeed brought in a sea-change for businesses, driving more engagement and increasing sales.
2. Image and Speech Recognition with Machine Learning
Ever tried searching for pictures of your dog with Google Photos? Or maybe tried using a voice assistant like Siri or Alexa? That’s magic by Machine Learning again.
Image recognition involves the classification of objects within pictures by machines and assigning them labels. For instance, Google Photos uses machine learning to enable searching within your photo collection for certain image classes such as “beaches” or “birthday cakes” without you needing to tag them yourself.
In turn, speech recognition gives the power of voice assistants like Siri, Alexa, and Google Assistant to understand and respond to human speech. The more you use it, the smarter it gets because, over time, it learns your voice patterns and preferences. Such technologies also have found their way into medical transcription, customer service chatbots-saving so much time and increasing efficiency.
According to Gartner, the image recognition market is about to grow big-time, and with the average machine learning engineer salary exceeding $120,660 per year, this is a very hot space for developers!
3. Fraud Detection
Have you ever gotten a call from your bank asking if some transaction that has been made using your card or account seems suspicious? That is machine learning working in the back to save your account!
The machine learning algorithms detect patterns as fraud detection systems sift through several thousands of transactions in real-time. Further, they flag any anomaly that could possibly indicate fraud. Here’s how it works:
| Fraud Detection Process | Machine Learning Role |
| Transaction Monitoring | Continuously analyzing transactions for unusual behavior |
| Pattern Recognition | Detecting deviations from normal patterns, e.g., purchases in another country |
| Flagging Suspicious Transactions | Alerting users or blocking transactions that don’t match normal behavior |
ML finds its most useful applications in banking and e-commerce, where numerous transactions take place every second. Over time, these machine learning models have grown increasingly accurate, and it is a lot harder now to let fraudsters slip through the cracks.
4. Self-Driving Cars
Until recently, self-driving cars came from science fiction. However, they’re now a reality with the use of machine learning! Companies like Tesla, Waymo, and Uber have reinvested this technology with heavy investments in autonomous car technology, enabling cars to learn and make decisions from the environment using machine learning.
Self-driving cars use computer vision powered by sensor fusion and neural networks. Patterns of obstacles-like pedestrians, traffic signs, and other cars-are learned by a self-driving car so that they can get around streets safely.
5. Healthcare and Medical Diagnosis with Machine Learning
Machine learning in healthcare is nothing short of revolutionary, be it the diagnosis of a disease or personalization of a treatment plan. Most big data analytics in medicine is assigned for analysis to ML algorithms, many times much quicker and more accurately than humans.
AI models have been trained in the detection of diseases like cancers from the imaging data available on MRIs and X-rays. In fact, IBM Watson Health applies machine learning to help doctors reach decisions faster and more precisely.
| Application | Role of Machine Learning |
| Medical Imaging | Analyzing MRI, X-ray, and CT scans for early disease detection |
| Predictive Analytics | Predicting patient outcomes based on historical data |
| Personalized Treatment | Creating tailored treatment plans by analyzing patient health data |
Machine learning has also started to play a major role in the development of new drugs, as it has hastened computational biology to find potential compounds for drugs. So, the next time you see a breakthrough in medicine, it is probably because of machine learning!
6. Natural Language Processing (NLP)
NLP is what makes machines understand, interpret, and respond in human language. If you have ever spoken to a chatbot or heard of Google Translate, then you have experienced NLP at work. It is one of the most exciting and fast-developing fields of machine learning.
Here’s a quick look at the NLP-powered applications:
| NLP Application | Machine Learning Functionality |
| Chatbots | Interacting with users and resolving queries in real-time |
| Sentiment Analysis | Understanding customer sentiments from reviews or feedback |
| Language Translation | Translating text across languages accurately and fluently |
Demand for NLP experts is skyrocketing, especially in the corporate world as it works to deliver personalized and efficient customer experiences. A machine learning course, including NLP, will introduce aspiring engineers into this fast-growing and emerging field.
Final Thoughts
From streaming services to self-driving cars, machine learning is revolutionizing how people live and work. It solves problems in record time, predicts things with greater precision, and lets companies provide more personal experiences. Whether you’re looking to line your pocket as a machine learning engineer or simply understand the tech better, knowing the different applications of ML is a great first step.
Now, with the world of machine learning expanding daily, this is an apt time to get yourself enrolled in a machine learning course and plunge head over heels into this already exciting field. Maybe you are the one to work out on the next big application that will change our everyday lives.Nail your machine learning interviews with Interview Kickstart’s Advanced Machine Learning Course! With 500+ FAANG instructors coupled with live training and mock interviews, Interview Kickstart will help you build the skills and confidence you need to land your dream job. Having trained over 17,000 tech professionals, they have a track record of success. Sign up for their free webinar today and learn about how Interview Kickstart can help you accomplish your career goals.
