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Machine learning has been enjoying prominence across the business landscape for quite some time now. It has paved the way for technological advancements like never before. Simply put, there are tons of applications that machine learning caters to, thereby, simplifying our lives. On that note, have a look at the top 10 most interesting machine learning applications of 2023.
Product recommendation system
Almost all the e-commerce websites that one can think of relying on product recommendation systems heavily. Machine learning turns out to be the base of such recommendation systems. The websites you visit track your behaviour on the basis of your previous purchases, searching patterns, and history, and then come up with recommendations.
Facial recognition
Facial recognition is a unique machine learning application that aims at interpreting and deriving information from visuals. Machine learning plays a pivotal role in facial recognition as it does everything from unlocking smart devices to identifying criminals.
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Enhancing cloud services
A lot of companies across the globe rely on machine learning to identify objects, people, text, and activities in both images and videos. This turns out to be no less than a blessing for developers and data scientists as they can build, train and deploy ML models without any hassle whatsoever.
Assessing creditworthiness
Machine learning has gained wide recognition for its ability to assess creditworthiness. Now, assessing the credibility of students as well as first-time credit card applicants who often do not have a credit history is no longer a problem as machine learning takes into account other factors like an applicant’s current financial health and habits.
Chatbots
No wonder chatbots have become an integral aspect of a business. Gone are the days when chatbots used to follow established scripts that base the machine’s subsequent actions on keywords. Today, chatbots have become way smarter as they understand humans like never before, handle requests better, and have more natural responses – thanks to machine learning.
Self-driving cars
This is one of the coolest and most innovative machine learning applications. The idea behind self-driving cars is that the model works on Deep learning and it crowdsources data from all of its vehicles and its drivers. We have reached a stage where self-driving cars have become increasingly popular across the globe.
Handwriting recognition
One cannot deny the fact that digitizing exam papers, and documents by hand are time-consuming as well as inaccurate. This is exactly where ML comes into play. ML stands for the ability to scan and digitize documents in minutes.
Google translate
Google translate is one of the most widely used machine learning applications. The technology is such that it makes use of machine learning to provide the most accurate translation of any sentence or word.
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Speech recognition
Speech recognition, the transformation of spoken words into text, is widely used in everyday life to create voice interfaces and voice assistants. This machine learning application has transformed our lives for the better, without a doubt.
Fraud Detection
Considering how rapidly the number of transactions has increased over the years as a result of credit/debit cards, smartphones, numerous wallets, UPI, and much more, it is important that we take every possible step to prevent fraud. Well, what better way of doing so than relying on machine learning?
Machine learning is the latest buzzword sweeping across the global business landscape. It has captured the popular imagination, conjuring up visions of futuristic self-learning AI and robots. In different industries, machine learning has paved the way for technological accomplishments and tools that would have been impossible a few years ago. From prediction engines to online TV live streaming, it powers the breakthrough innovations that support our modern lifestyles.
What is Machine Learning?
Machine learning is an umbrella term for a set of techniques and tools that help computers learn and adapt on their own. Machine learning algorithms help AI learn without being explicitly programmed to perform the desired action. By learning a pattern from sample inputs, the machine learning algorithm predicts and performs tasks solely based on the learned pattern and not a predefined program instruction. Machine learning is a life savior in several cases where applying strict algorithms is not possible. It will learn the new process from previous patterns and execute the knowledge.
One of the machine learning applications we are familiar with is the way our email providers help us deal with spam. Spam filters use an algorithm to identify and move incoming junk email to your spam folder. Several e-commerce companies also use machine learning algorithms in conjunction with other IT security tools to prevent fraud and improve their recommendation engine performance.
Let’s explore other real-world machine learning applications that are sweeping the world.
Popular Machine Learning Use Cases and Examples
1: Which of the following algorithms is most suitable for classification tasks?
1. Social Media Features
Social media platforms use machine learning algorithms and approaches to create some attractive and excellent features. For instance, Facebook notices and records your activities, chats, likes, and comments, and the time you spend on specific kinds of posts. Machine learning learns from your own experience and makes friends and page suggestions for your profile.
2. Product Recommendations
Product recommendation is one of the most popular and known applications of machine learning. Product recommendation is one of the stark features of almost every e-commerce website today, which is an advanced application of machine learning techniques. Using machine learning and AI, websites track your behavior based on your previous purchases, searching patterns, and cart history, and then make product recommendations.
3. Image Recognition
Image recognition, which is an approach for cataloging and detecting a feature or an object in the digital image, is one of the most significant and notable machine learning and AI techniques. This technique is being adopted for further analysis, such as pattern recognition, face detection, and face recognition.
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4. Sentiment Analysis
Sentiment analysis is one of the most necessary applications of machine learning. Sentiment analysis is a real-time machine learning application that determines the emotion or opinion of the speaker or the writer. For example, if someone has written a review or email (or any form of a document), a sentiment analyzer will instantly find out the actual thought and tone of the text. This sentiment analysis application can be used to analyze a review based website, decision-making applications, etc.
5. Automating Employee Access Control
Organizations are actively implementing machine learning algorithms to determine the level of access employees would need in various areas, depending on their job profiles. This is one of the coolest applications of machine learning.
6. Marine Wildlife Preservation
Machine learning algorithms are used to develop behavior models for endangered cetaceans and other marine species, helping scientists regulate and monitor their populations.
7. Regulating Healthcare Efficiency and Medical Services
Significant healthcare sectors are actively looking at using machine learning algorithms to manage better. They predict the waiting times of patients in the emergency waiting rooms across various departments of hospitals. The models use vital factors that help define the algorithm, details of staff at various times of day, records of patients, and complete logs of department chats and the layout of emergency rooms. Machine learning algorithms also come to play when detecting a disease, therapy planning, and prediction of the disease situation. This is one of the most necessary machine learning applications.
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8. Predict Potential Heart Failure
An algorithm designed to scan a doctor’s free-form e-notes and identify patterns in a patient’s cardiovascular history is making waves in medicine. Instead of a physician digging through multiple health records to arrive at a sound diagnosis, redundancy is now reduced with computers making an analysis based on available information.
9. Banking Domain
Banks are now using the latest advanced technology machine learning has to offer to help prevent fraud and protect accounts from hackers. The algorithms determine what factors to consider to create a filter to keep harm at bay. Various sites that are unauthentic will be automatically filtered out and restricted from initiating transactions.
10. Language Translation
One of the most common machine learning applications is language translation. Machine learning plays a significant role in the translation of one language to another. We are amazed at how websites can translate from one language to another effortlessly and give contextual meaning as well. The technology behind the translation tool is called ‘machine translation.’ It has enabled people to interact with others from all around the world; without it, life would not be as easy as it is now. It has provided confidence to travelers and business associates to safely venture into foreign lands with the conviction that language will no longer be a barrier.
Your model will need to be taught what you want it to learn. Feeding relevant back data will help the machine draw patterns and act accordingly. It is imperative to provide relevant data and feed files to help the machine learn what is expected. In this case, with machine learning, the results you strive for depend on the contents of the files that are being recorded.
Application of Unsupervised Learning Examples
Unsupervised learning enables systems to identify patterns within datasets with AI algorithms that are otherwise unlabeled or unclassified. There are numerous application of unsupervised learning examples, with some common examples including recommendation systems, products segmentation, data set labeling, customer segmentation, and similarity detection.
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Application of Reinforcement Learning Examples
Reinforcement learning is also frequently used in different types of machine learning applications. Some common application of reinforcement learning examples include industry automation, self-driving car technology, applications that use Natural Language Processing, robotics manipulation, and more. Reinforcement learning is used in AI in a wide range of industries, including finance, healthcare, engineering, and gaming.
Machine Learning for Algorithmic Trading
Algorithms are a significant part of machine learning, and this technology relies on data patterns and rules in order to achieve specific goals or accomplish certain tasks. When it comes to machine learning for algorithmic trading, important data is extracted in order to automate or support imperative investment activities. Examples can include successfully managing a portfolio, making decisions when it comes to buying and selling stock, and so on.
Conclusion
Popular machine learning use cases and technology are evolving at a rapid pace, and we are excited about the possibilities that our Machine Learning course has to offer in the days to come. As the demand for AI and machine learning has increased, organizations require professionals with in-and-out knowledge of these growing technologies and hands-on experience.
