If I say, ‘Machine Learning Application’, it seems to be a very high-fi term, not for everybody. But if I say, machine learning is the base for Artificial Intelligence (AI), then…In addition to that, all of us are already using it this way or that, then…Now, your curiosity may have engaged here! How? When? And where? The answer is, ‘In the form of applications and/or AI directly or indirectly.

So, just following your line of curiosity and interest, let’s know more about the Top 10 Machine Learning Applications and Examples in 2022.

Curtain Raiser:

From SIRI, Alexa to self-driven cars, AI is slowly becoming part of our lives. To recognize artificial intelligence, machine learning is essential. Machine learning actually is the subset of artificial intelligence.

Machine learning in simple words is nothing but enabling the computer systems with their ability ‘to learn by themselves without thorough specific programming. Furthermore, these software applications use data history as inputs, learn from it and give new outputs or predictions. It is the method of training the algorithms to the machines so that machines can learn to make decisions.

However, it’s true that no machine or no program can show as much flexibility as a human. But, machine learning algorithms have proved that the artificial intelligence incorporated by them can be as precise and versatile as a human being. 

Importance of Machine Learning Applications:

Machine learning is important because,

  • It provides companies and enterprises with an overview of current trends and customer behavior.
  • Accordingly, It also provides information about the operational business patterns.
  • All this ultimately supports the development of new products.

Many leading companies like, Facebook, Google and Uber have machine learning as the center of their operations. Thus, machine learning applications serve as a preeminent competitive differentiator for many companies.

As we proceed through the top 10 machine learning applications and examples, you will better understand their role in various spectra of our life and businesses as well. 

So, without any delay, let’s walk through the top 10 machine learning applications.

Top 10 Machine Learning (ML) Applications with Examples.

1.  Machine Learning Applications in Social Media.

Various social media have become essential for today’s generation. Ultimately, there are billions of users all over the world and the number has been ever-increasing only. Machine learning serves as a center point to such a gigantic number of users for their personalized news feeds and their personalized targetted ads. 

For example, social media and chat applications are so upgraded that we need not call or open our mail for a product. We just have to leave a comment and we will get a rapid response on Meta or Instagram; faster than the traditional channels.

Machine Learning Application in Meta: Face Detection and

Face Recognition

Auto-tagging feature of Meta is the most popular one. How does it do? With face recognition and identification. Let me explain. Meta identifies our friend’s pic with some already tagged his or her pics via face recognition algorithm with around 98% accuracy; comparable to human ability.

 However, image recognition technology has various specific tasks in different fields like those of self-driving cars or policing as per priorities. It follows 3 basic steps; Detection, Classification and Recognition. 

For Example, a system will detect a face, classify it as a human face and recognize it as my face and then unlock my smartphone or any security door. Here, the algorithm is designed to recognize the patterns. It sorts out among many other patterns to recognize the specific image.

Some other examples are:

  • Recommendation engine of the professional social network, Linkedin. It gives us recommendations of new jobs, where we should apply next, to whom should we catch up, which professional groups should we join, where our skills match, etc.
  • Twitter ranks posts in user timelines. It uses deep learning models (deep learning is the subset of machine learning). After gathering all the twits, accordingly, the related model assigns a score to each tweet. Furthermore, depending on the score from high to low the tweets are appeared.

 2. Machine Learning Applications in Travel.

Features brought by machine learning in the travel industry, have revolutionized the services for travelers and manifold increase in business for the travel service providers. These machine learning applications with examples are as follow

For Compatible Prices and Fare:

All of us very well know Uber. It tells us the price of our ride in advance. Also, it suggests the ride-share by matching the routes of the passengers. It also minimizes the waiting time after booking the ride. How does it do all these things? Aren’t all this come under good services? Won’t this increase the business? Yes, Of course! They have already done it.

This is achieved by Uber’s machine learning application ‘Geosurge’. It uses real-time traffic patterns, demand and supply. So, it can also predict in which area there would be high demand. Accordingly, the drivers can be informed to be prepared. This in turn reduces surge pricing. Uber has its patent on surge pricing.

For Travel-Sentiment Analysis:

Sentiment analysis is one of the most prominent features of machine learning techniques. Its complex model uses Natural Language Processing (NPL) and machine learning algorithms to analyze gigantic data. Amadeus IT group predicts that around 90% of Americans travel with their smartphones, share their pics and travel stories on social media and review websites. Also, Tripadvisor receives 280 travelers’ reviews per minute. That means such a bulk influx of data from 390 million unique travelers and 435 million customer reviews. So, Tripadvisor carries out a sentiment analysis on this data to improve its services. Machine learning applications at Tripadvisor aim to ponder brand-related review data.

3. Machine Learning Application in Retail.

In the retail industry, there is a mega influx of data from the customers, their choices, demands and also supply from the market, etc. So, they need to analyze this data in real-time. Furthermore, they need to estimate the insights which in turn provides tangible outputs like repeating the purchase. Deep learning and machine learning fulfill these requirements for retailing giants like Amazon, Alibaba, Target and Walmart.

Product Recommendations.

Customers are always satisfied with the personalized purchase 

experiences. Product recommendation evokes retailer’s conversion rate thereby enhancing the business.

  • Amazon: Recommendations like “Customers who viewed this also view”; “Frequently bought together”. Additionally, personalized product recommendations are on the home page and via mail. Amazon uses Artificial Neural Networks and machine learning algorithms for these product recommendations for its customers.
  • Alibaba: Uses “E-commerce Brain” for real-time data analysis, prediction of choices and recommendations.
  • Media like Netflix, and Youtube: Use recommendation engines to suggest to viewers their favorite shows, and videos.

4. Machine Learning Applications in Healthcare.

When technology helps healthcare and medication; healthcare gets revolutionized. Machine learning is no more exception for it. 

Machine learning applications in medical fields will,

  • Enable doctors to predict the fate of patients with vital diseases.
  • Be able to help patients to save money by avoiding unnecessary tests.
  • Replace the radiologists.
  • Boost the efficacy of clinical trials.
  • Be able to generate new inventions worth $100 million.

Computers and robotic medical procedures can’t replace human doctors and nurses but definitely get revolutionized to high precision and perfection in scarcity of them.

Machine Learning Application in Drug Discovery

New drug discovery is a costly affair. Thousands of drug elements have to be tested, and millions of combinations have to be tried. To handle and analyze these kinds of data, predictions, suggestions and decision-making can be very well supported by machine learning applications.

For example, Pfizer is using a machine learning-supported application, IBM Watson, in its immuno-oncology (a method that enables the human immune system to fight against cancer by itself) research. The model of machine learning here is helping to strain the data, particularly related to the combination of drugs for years to facilitate the research.

Machine Learning in Personalized Medication/Treatment

In hospitals, machine learning not only makes the personal data history of a patient handy but also, his family history, general medication regarding the same problem and the latest research as well. Furthermore, it not only matches all the data but also compares it. So, whatever medication and treatments are given to the patient are highly precise and personalized. This thing is but obvious.

Other than that, there are many unforeseen aspects as, 

  • Personalized treatment has of great significance in the future in terms of identifying genetic markers and genes responding to the specific treatment. And machine learning will play a starring role in this kind of research.

For example, two companies, Genentech and GNS Healthcare collaborate to innovate and find out solutions and treatments using biomedical data. They use Forward Simulation Technology and GNS Reverse Engineering to determine patient response to gene markers which may lead to the target therapies.

  • Such personalized treatments will also save the patient’s costs of treatments and help to maintain his health to the optimum.

5. Machine Learning Applications in Finance

Machine learning (ML) and artificial intelligence (AI) have brought up a new, polished and safer revolution in the banking sector. Thereby, banks are able to provide personalized customer services at low costs, better compliances as well as significant generation of revenue at the same time. 

Fraud Detection

Due to digitalization, everything is digital now. We are used to online shopping and payment modes. But with the ease, fraudulent transactions have also increased. Here, the machine learning algorithm provides the pattern of the expenditures of a person. So, if any transaction normally does not match the pattern, immediately send an alert so as to avoid fraudulent transactions. 

In fact, preventing and reducing fraudulent transactions is the main significant role of machine learning applications in the banking sector. At every transaction of every customer, the personal expenditure pattern data is analyzed in real-time and a fraud detection score is generated. If it’s a fraud transaction, the transaction is ceased and transformed for manual review. Furthermore, if the score passes the threshold limit, payments and transactions are directly rejected automatically. And all this happens within no time which is impossible to do manually. Again, every minute million of transactions are going on. Such gigantic data, patterns and analyses!!

For Example,

  • Collaboration of Citibank and Freedzi, a fraud detecting company that detects and prevents frauds in online and in-person banking in real-time and alerts the customer.
  • Pay Pal uses machine learning to resist money laundering. Pay Pal has several machine learning tools and features. So, it can accurately determine between legitimate and fraudulent transactions.
  • For credit card fraud detection, machine learning applications like K-means, Random forest, Decision Trees, etc. are being developed.

Loan Eligibility Prediction

For banks, interest from the loans is the source of profit. For that, the proper validity and eligibility of a person who can repay it should be determined. 

The Loan Eligibility Prediction, a project in machine learning enables the banks in forecasting the eligibility of a customer to repay a loan. Thus, avoiding the unbanked clients’ access to financial services.

Other Machine Learning Applications in Finance

In other finance sectors also, machine learning applications are used. A few examples are,

  • Time Series Analysis for stock market predictions.
  • Auto Encoders for credit card anomaly detection
  • GCP for loan eligibility forecast.

6. Machine Learning Applications in Traffic Prediction

In today’s world, time is very crucial. It’s very important to be on time. But traffic jams are the major hurdle for that. It becomes more crucial in any kind of emergency.

To help this out and minimize the nuisance, machine learning has played a key role. Again here needs large traffic data and wherever large data comes, its real-time analysis; machine learning applications can be employed. 

For Example,

Traffic prediction feature in Google maps. Suppose, it shows a general traffic speed of around 50kmph in a particular area from 9 am to 5 pm. This speed reduces to around 30kmph at around 6 pm. 

Machine learning algorithm combines and compares with the current traffic situation for prediction and recommendation. Not only that, while doing this, it also considers various factors like accidents, speed limits, road closures, road quality and Supersegments, that is, road networks; are also considered. It always suggests a better available route option with respect to these factors.

7. Machine Learning Applications in Smart Manufacturing   

We already know about automation and computerization in the manufacturing industry. The use and application of IT in factories are called ‘Industry 4.0” But, it’s just not the automation. It’s the smart decision-making, adoption of the industrial processes and interaction with the environment through machine learning and AI.

So, here comes the machine learning employment in smart manufacturing.

Quality Control and OEE

Machine learning enhances Overall Equipment Effectiveness (OEE). It determines the performance, availability and quality of the assembly equipment quickly. Additionally, it also identifies the flaws and minimizes the errors by itself.

Manufacturing of Optimal Semiconductor 

Optimized use of machine learning applications in semi-conductor manufacturing can yield up to 30% by reducing the scrap rates. 

Testing costs can be reduced by using root-cause analysis through streaming and manufacturing workflows. Additionally, machine learning equipements are also cost-effective in terms of around 10% less annual maintenance cost, 25% less inspection cost and 20% less downtime.

Optimizing Supply Chain

Any organization has its status and value due to its up-to-date and quick logistic solutions. Machine learning here also helps to optimize logistics such as inventory management, asset management and supply chain management. Combining Io T and Ai is very crucial for any company to understand as well as improve its supply chain; that is, asset tracking, supply chain visibility and inventory management.

An example of machine learning in manufacturing is the ‘digital twin’ concept used by General Electric.

The digital twin is the coded, data-based mimic of an industrial machine or equipment, that is an engine, gas turbine or metalworking machine. Io T sensors collect data like vibration level, noise, temperature output, etc. and send it to the cloud. There, data is processed to model ‘twin’ and replicated to the performance of that real equipment.

8. Machine Learning Applications in Real Estate Price Prediction   

Real estate is the largest asset market in the world because everyone needs a home to live in. Other than that, it’s a good traditional investment option. Machine learning applications in the real estate business have already proven their importance. Machine learning is successful in the reviews like,

  • Which real estate should one buy to receive maximum returns?
  • When can one expect a drop or reasonable pricing in the real estate market?
  • Where should one prefer to buy an estate and when?

Other than these, the data also includes the following factors to give

 suggestions.

  • Zip code
  • The population density in the area
  • Duration of construction
  • Floor area
  • Distances to restaurants, food markets, grocery shops, etc.
  • Proximity to schools, colleges, shopping centers and entertainment centers.
  • Construction cost
  • Currency exchange rate
  • Average rating of restaurants and cafes in the area.

Therefore despite all this bulk data of each and every property available, the price predictions and suggestions are just near too accurate. Even if it has to predict similar properties in the same area, it differentiated them well and specifically suggest the preference.

9.  Machine Learning Applications in Agriculture

In many advanced countries, agriculture has already started benefitting from artificial intelligence and machine learning applications. It helps in increasing a farmer’s profit by increasing crop yields and reducing human labor needs.

Machine learning is used in agriculture,

  • To determine irrigation requirements of the field. Here, the procedure includes,
  • First, capturing areal footage of the field
  • Then processing of this footage with the machine learning program.
  • Thus, from that identifying underwatered and overwatered areas for irrigation.

For this machine learning models generally use the amount 

of water content in leaves; that is, LWC.

  • To detect damaged plants and trees due to pests or any other kind of damaged areas in the field.

For Example,

To detect the damage, firstly the AI model is trained with the healthy and affected plant images; both. Then a drone employed with AI camera undergoes regular field monitoring and collects all the pics. Now, these pics are analyzed by the AI model pixel by pixel to determine the damage. So, it becomes very easy for the farmer to take quick action to avoid further damage and loss.

  • Furthermore, to save resources like water and fertilizers.
  • For automatic weeding
  • Also, for soil health monitoring, etc.

10. Machine Learning Applications in Surveillance

Video surveillance has already been known to every Tom, Dick and Herry. It’s actually the most commonly known application of machine learning. These video cameras record the view, detect the objects, classify them and determine their properties.

Machine learning has added new facets to surveillance and added extra keen security. It helped to overcome the issues in traditional surveillance systems.

Machine learning aims here for,

  • Detecting people entering a restricted area.
  • Identifying possible criminals or shooters and sending real-time alerts to the immediate responder.
  • Detecting abnormal behavior and suspicious activities in public areas.

Machine learning models here can perform the tasks without human 

assistance and supervision with accuracy and also send warning alarms.

Henry Harvin

If this manifestation of hidden truth is quite enough to stretch your curiosity and interest in the field of machine learning as a career,  you may pursue a course in it. In fact, machine learning applications have a very luring future. One of the best institutes for machine learning courses is Henry Harvin.

Benefits of Machine Learning Course at Henry Harvin

Henry Harvin offers the following benefits.

  • To provide real-time experience, we cover 5+ practical projects in machine learning with different domains like HR, Sales. Business, Marketing and Finance.
  • The course itself is a 9-in-1 array including training, project, internship, certification, placements, E-Learning, boot camps, hackathons and 1-year free gold membership.
  • Experience the actual industrial projects.
  • Weekly 10+ job opportunities.
  • Free Bootcamp sessions for 1 year.
  • Improve the knowledge of Computer Graphics.
  • Develop the skills of Python Web Framework used for back and web programming.
  • Thorough information on the fundamentals of Python to the Real World Applications.

In a Nut-Shell

It’s a fact that, with the help of machine learning and artificial intelligence, everything is being smarter and smarter. The once dream or fantasy has come to reality only due to machine learning and AI. They have not only opened up new horizons of services and facilities offered but also new opportunities. I have covered machine learning applications in various industries with already existing examples. But still, there are lots of unforeseen applications of machine learning which are yet to be discovered. And the sky is the limit for that!!

So readers, from this blog explore some more information to enjoy knowing how smart technology; even in a small device, works! Question yourself, ‘Which machine learning model is used and how does it work?’ And if you are a student interested in computing; think about machine learning applications as your career! Best Wishes and Happy Reading!

Recommended Reads:

1. Machine Learning and Artificial Intelligence: How and Where They  Interconnected in 2022.  

2. AI Vs Machine LearningVs Deep Learning in 2022.

3. What is Machine Learning in 2022?

4. Python for Data Science and Machine Learning in 2022.

5. Data Science Vs Machine Learning in 2022.

FAQs

1. Is Machine learning a good career option?

Ans. Yes! Definitely!! As everything is being smarter, machine learning is in high demand. So, it’s a good career path.

2. Do Machine Learning jobs pay well?

Ans. Yes! Machine Learning jobs pay really well.

3. Is Machine Learning difficult to learn?

Ans. Machine Learning implies difficult algorithms. So, in the beginning, it can be cumbersome to handle each algorithm with separate components.

4. Does Machine Learning have a good future in India?

Ans. Yes! In growing digital India, with industrial and infrastructural advancements, Machine Learning has a very good future in India.

5. What pays more Machine Learning or Data Science?

Ans. Both are equally in demand. So, both get near about same rame range of salaries. Still, Machine Learning is a little higher side than Data Science.

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