Real Life Applications of Machine Learning
In this tutorial, we will see some of the real life applications of Machine Learning. Let’s start!!
Machine Learning Applications
Machine learning is a huge step for the technology industry, towards the future. It has become so prevalent in the last decade that we come across applications of machine learning in almost every aspect of everyday life. This article focuses on some of those applications:
1. Product recommendation
You may have noticed that sites like Amazon or Myntra tend to recommend products that somehow suit your taste or things that you’ve been talking about wanting. These are recommendation systems at work. They offer recommendations based on your shopping history, brand preferences, items in cart etc.
2. Chatbot consumer support
Most websites offer the option of chatting onsite to receive help or offer feedback. While some sites employ humans, most employ machine learning based chatbots. They glean important info from you and use that to tailor their responses to provide you with ultimate satisfaction.
3. Customer feedback analysis
Sentiment analysis can be applied to feedback from customers to glean how they feel about the product or text categorization may be used. These are applications of ML.
4. Self driving cars
Tesla made waves recently with the launch of its self-driving cars. These use deep learning and collect data from all vehicles and their drivers. Deep Learning can be loosely called a subset of machine learning
5. Virtual personal assistants
Various virtual assistants like Alexa, Siri and Cortana are a part of everyday life now. They wake up with a trigger word (eg: “OK Google”) and assist with literally anything from booking movie tickets to setting reminders for important events. They use an array of ML algorithms to function properly.
6. Email spam filtering:
Most email providers automatically filter spam emails to protect you from scammers and to restrict the amount of unnecessary emails in your inbox. Multi Layer Perceptron, C 4.5 Decision Tree Induction are some of the spam filtering techniques that can be used.
7. Navigation:
If you have used Uber, Ola or any of their competitors, you are already familiar with their user experience. These apps use ML to automatically detect your location and traffic patterns in your city and manage to find the shortest possible path.
8. Stock market prediction
Stock market price prediction used to seem like an impossible task as the market kept fluctuating. But ML algorithms have made it possible for traders to analyse market trends and invest accordingly.
9. Videos Surveillance
Video surveillance systems are useful to detect crimes before they occur. They monitor strange behavior such as individuals standing immobile for lengthy periods of time, tripping, or dozing on benches, among other things. When such actions are reported and verified, they aid in the improvement of surveillance services. This occurs as a result of machine learning working in the background.
10. Search Engine Result Refining
Machine learning is used by Google and other search engines to improve your search results. Every time you do a search, the backend algorithms keep track of how you react to the results. If you open the top results and stay on the page for a long time, the search engine considers that the results are relevant to your query. Conversely, if you don’t open any of the results, the search engine assumes that the results provided did not meet your criteria and improves itself.
11. Online Fraud Detection
Criminals have honed their skills in spotting loopholes in financial processes. But even the smartest criminal cannot outsmart AI. When a consumer completes a purchase, the Machine Learning model examines their profile in detail, looking for worrisome tendencies. Issues like fraud detection are generally posed as classification problems in Machine Learning.
12. Image Recognition
Image Recognition is a technique for categorizing and detecting a feature or an item in a digital image, and it is one of the most important and well-known Machine Learning and AI approaches. Pattern recognition, face detection, and face identification are some of the applications of this approach.
13. Sentiment Analysis
Sentiment analysis is a real-time machine learning programme that detects the speaker’s or writer’s sentiment or opinion. For example, it allows companies to analyse their reviews (or any other type of document), by instantaneously determining the text’s true meaning and tone.
14. Access Control
Companies use ML algorithms to determine the level of access that each employee would have to their solutions and sensitive information. This reduce errors and improves efficiency while also protecting data privacy
15. Google Translate
Moving to a new place is really scary, especially if you aren’t familiar with the language spoken there. Google Translate has helped solve that problem using its neural network (GNMT) that works on thousands of languages and word sets.
16. Movie recommendation
Netflix, Prime and other movie streaming platforms use recommender systems to suggest content that each user would enjoy. They do this based on ratings, viewing history, keywords, genre and other such data.
17. Diagnosis of disease
Machine learning algorithms like classification and association are useful to help doctors diagnose patients. Machine intelligence has started a new wave in the medical field.
18. Dynamic pricing
Economists have been working very hard to deduce a fool-proof formula to set the right price for a service or product. Machine learning has taken away the need for that. We can use algorithms to track environmental variables and buying trends and use that data to decide price, dynamically.
Summary
Necessity is the mother of invention. As more problems are encountered, more solutions keep getting formed, all around us. ML is just a tool that is useful to solve problems. All that is required to identify machine learning solutions around us, is a curious eye and thirst for knowledge.