Top 10 Common Applications of Machine Learning- Great Learning

Top Machine Learning Applications in 2023

Dynamic Pricing
Transportation and Commuting
Fraud Detection
Virtual Personal Assistant
Social Media
Instant Translation

Machine Learning, a sub-branch of Artificial Intelligence, has established itself as the new go-to technology for businesses worldwide. Whether it is e-commerce or healthcare, almost all the industries are using Machine Learning extensively to make futuristic solutions and products. Machine Learning depends heavily on programs and algorithms that help machines self-learn without having to be instructed explicitly. Machine Learning is pretty much dictating our daily lives- how, you wonder? Let’s look at the top applications of Machine Learning to understand how it is shaping the digital economy.

1. Dynamic Pricing

As crucial as it is, pricing strategy is one of the oldest puzzles of the modern economy. Whether it is the entertainment industry or the consumables industry, efficient product pricing is essential for profit margins and affordability. Depending on the objective, there are several pricing strategies that businesses can choose for sales and marketing. However, choosing the right pricing strategy is easier said than done. Several decisive factors like cost of production, consumer demographics, demand curve, market control, value and more need to be adequately aligned for any product to be priced properly. Thankfully, Artificial Intelligence has effectively resolved this issue in recent times. AI-powered pricing solutions have helped businesses understand consumer purchasing behaviour and price their products accordingly.

Machine learning tools use insights from data to create logic. This process improves with the amount of data that is fed to the machine learning system — the more the data, the better the results. Without using direct programs, these machine learning softwares use humongous amounts of data to improvise and deliver accurate pricing strategies. Machine learning algorithms use extensive data analysis to find optimised solution functionality. These softwares use various ML pricing models like granular customer segmentation with cluster analysis and competitor and attribute-based pricing and KPI-based pricing to reach an optimised pricing range.

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2. Transportation and Commuting

All the taxi-booking, vacation planning apps that you use run on machine learning. Whether it is customer experience or demand-supply gap, machine learning systems use data to manage and optimise the booking process. While using a ride-booking app, you must have come across recommended destinations. Machine learning algorithms use historical data to understand the most frequently travelled routes and provide suggestions accordingly. Apps like Uber and Ola use extensive data analysis to predict time and areas of demand. Once the app calculates the demand, drivers are alerted so that they can offer rides for that particular area. This is how ride-hailing companies manage the demand-supply gap. Machine learning algorithms also reduce ETA by recommending the fastest routes in real-time. For peak hours, this demand-supply predictions work by suggesting higher prices to make these services profitable.

Vacation planning apps use the same system to recommend the cheapest flight fares, hotel bookings, and more.

3. Fraud Detection

While the vast amount of data available on the internet makes for a great case of data studies and analysis, it also increases the chances of fraudulent activities. Machine learning is emerging as an effective technology to secure our cyberspace. Supervised and unsupervised ML models are being used to detect different kinds of online frauds, ranging from spotting anomalous behaviour to preventing money laundering. Even the entertainment and media industry is facing undeniable problems with online frauds. Fake news is a big issue today that can disturb the economic and political situation of any nation. ML semantic analysis studies structured, unstructured and table-type data to detect fake claims and news. ML algorithms also look through existing repositories of news to find similar claims and validate the authenticity of any news piece. To understand more about fake news, check out this comprehensive course on fake news detection using machine learning.

Same holds true for online scams and identity threats. Fraud analysts across industries rely heavily on machine learning tools to investigate claims, news and more.

4. Virtual Personal Assistant

Virtual personal assistants have surfaced as one of the most significant finds of the 21st century. Machine learning algorithms have done phenomenal work in the field of speech recognition, natural language processing, text to speech and speech to text conversion. Once you ask them a question, they scan through the internet to find you relevant answers. In addition to that, they also keep track of your schedule, goals, and preferences to recommend relevant information. These virtual personal assistants feed on all your queries and inputs ( asking about the weather or the traffic) to continually improve and self-learn.

ML algorithms collect and refine information on the basis of any user’s past behaviour. This process helps in customising results according to the user profile.

With more than 2.5 billion active users every month, social media platforms like Facebook and more are some of the biggest communities today. Social media has become an indispensable part of our lives. Targeted ads, friend suggestions, and personalised news feed are a few of the ways in which machine learning algorithms are improving our experience. Machine learning algorithms go through your profile to understand the friend requests you send, friends you connect with, groups you join, your interests, and based on that provide suggestions on who you can become friends with. Similarly, for Pinterest, ML algorithms recommend similar pins based on the objects (pins) you have pinned in the past. Computer vision, a subset of machine learning, scans through images to identify objects and patterns and uses this data to create recommendations.

Computer vision is also used for the face recognition feature in Facebook and Google. Every time Facebook asks you to tag yourself in a photo, it is because computer vision has scanned through your facial features to recognise the features unique to you. Once the ML systems have collected sufficient data on your facial features, it can accurately suggest the tag.

6. Instant Translation

Google Translate and other such apps are making language barriers a thing of the past. Apps like Google Translate and iTranslate use machine learning algorithms to make translation as accurate and semantic as possible. The ML programs too have evolved from rudimentary levels to include complex sentence structures and broader contexts.

Google Neural Machine Translation uses Natural Language Processing to self learn from numerous languages and exhaustive dictionaries to translate languages correctly. It also uses techniques like NER (Named Entity Recognition), Chunking, POS tagging and more to understand language intonation and deliver the most relevant translation. Translation techniques include:

Dual learning: Texts are translated back and forth from one language to another repeatedly until a natural, accurate translation is delivered.

Deliberation Networks: Similar to dual learning, this method involves translating the same text over and over again to improve the final results.

Agreement regulation: ML algorithms read text from left to right and then from right to left again to create a match. The end result is a consensus from both directions to eradicate errors.

Machine learning is a type of AI that allows computers to learn from data, without being explicitly programmed. The demand for machine learning specialists is growing rapidly, with an estimated 50% increase in demand by 2020. Machine learning can be used for a variety of purposes, such as improving business processes, developing new products or services, reducing costs, and more. Businesses that don’t adopt machine learning will likely fall behind their competitors.

Machine learning is one of the most popular and in-demand fields in technology today. With the increasing demand for machine learning experts, businesses are finding it difficult to fill positions with qualified candidates.

One reason for this high demand is that machine learning is used in so many different industries. As more and more businesses start to adopt machine learning, the demand for experts will only continue to grow. In this article, we will learn about the various real world applications of Machine Learning contributing to its rising demand.

But first learn What Machine Learning exactly is:

Machine learning is a process of teaching computers to learn from data, without being explicitly programmed. It’s a subset of artificial intelligence that enables computers to learn how to do things on their own by analyzing data. Machine learning is a process of teaching computers to learn from data without being explicitly programmed. It has been around for a while, but it is becoming more popular as businesses are starting to see the potential benefits.

Machine Learning application in risk analysis:

Machine learning enables computers to learn on their own by analyzing data, without being explicitly programmed. Machine learning is used in a variety of industries to predict outcomes and risks. Machine learning has many applications in business, including risk analysis. Risk analysis is the process of assessing the likelihood and impact of potential risks to a company. Machine learning can be used to improve risk analysis by automating the process of identifying and assessing risks. It can also be used to predict the likelihood of a risk event occurring, and to recommend mitigation measures.

For example, banks use machine learning to predict whether a customer is likely to default on a loan. Insurers use machine learning to predict the likelihood of an insurance claim. Retailers use machine learning to predict customer behavior and preferences.

William Cannon the Founder of Signaturely says,

I want to share my expert opinion. I am a CEO who is utilizing machine learning in executing tasks.

Machine learning brings out the power of data in new ways. Machine learning, as a branch of artificial intelligence, helps to improve computer systems by developing computer programs that can automatically access data and perform tasks via predictions and detections.

Machine learning eliminates manual data entry. Through its predictive modeling algorithms, relative errors during data entry can be avoided. The algorithm will detect the error and significantly correct it automatically. Hence, this has improved the productivity of workers, utilizing most of the time on carrying more tasks and not by focusing on the correction of the following errors.

With the aid of machine learning, industries reduced the risks of corrective maintenance practices that are very costly. Machine learning is being used in predictive maintenance. It helps technicians by detecting the issues in advance and resolving problems before equipment failure may occur. Preventive maintenance sensors include vibration analysis, oil analysis, and equipment observation.

In social media, machine learning can be used to automatically detect and respond to certain events, such as a natural disaster.

Social media platforms are increasingly using machine learning to better understand their users and provide them with more relevant content. Facebook, for example, uses machine learning to power its News Feed algorithm. The algorithm takes into account a wide range of factors, including user interests, past behaviors, and relationships with other users. This allows Facebook to show users the content that they are most likely to be interested in.

Another example of how machine learning is being used in social media is the development of chatbots. Chatbots are computer programs that can mimic human conversation. They are commonly used to simulate a human’s conversation with a customer service representative. However, chatbots can also be used for more complex tasks, such as automatically detecting and responding to negative sentiment on social media.

Steven Walker who is a Machine Learning expert working with Spylix states,

With my experience & knowledge, I would like to say that,

Machine learning is a rapidly growing technique in today’s technologies, and it has always been a buzzword. Here are some of the real-world applications of machine learning as of now:

Image recognition is one of the well-known applications of machine learning and is at the top for the most practical usages. It involves face detection, recognition of images, and automatic suggestions for tagging friends. Facebook uses this application to recognize the algorithm and suggest friends for a user.

We all would have used voice recognition at some point, whether it be Alexa, Google Assistant, Cortana, or Siri. Speech recognition is converting the commands given in the form of speech to text and responding accordingly. All these tools use machine learning to carry out their functions.

Traffic prediction in Google Maps uses machine learning. It sorts the traffic into three categories, namely, clear, moderate, and high traffic, based on the user’s real-time location and the average time taken for clearing the traffic in the past. It takes every user’s data and uses it to improve the tool’s performance.

Machine Learning Applications in Marketing:

Machine learning can help marketers better understand their customers. In the marketing world, machine learning can be used to predict customer behavior, personalize communications, and target ads. Machine learning algorithms are able to analyze large amounts of data very quickly, and can improve over time as they receive more data. This allows marketers to create targeted content and ads that are more likely to be successful. Machine learning is still in its early stages, so there are many opportunities for businesses to use it to improve their marketing campaigns. This makes them an ideal tool for marketing purposes, as they can be used to predict customer behavior and preferences.
Devin Schumacher, the Tech CEO of SERP who is also a Digital Marketer, says,

Machine learning has been critical in digital marketing and SEO developments in the 21st century and in our business.

ML for More *Granular Customer Segmentation*

We are now able to connect to customers via email at an individual level if we use machine learning. Traditionally, marketers segment customers based on age, gender, and location. It is not good enough these days, and ML can allow hyper-personalized campaigns instead.

NLP algorithms for studying content and customer interactions

We also study the recurrent topics and themes from the content, especially feedback coming from customers.

On the client side, I usually also use ML to study the themes of customer industry content before creating an SEO or digital content strategy.

Machine Learning for Inventory management and logistics:

In the context of inventory management and logistics, machine learning can be used to predict future inventory needs, schedule delivery trucks, and optimize warehouse space. There are many commercial applications of machine learning for inventory management and logistics. For example, Walmart is using machine learning to predict customer demand and allocate inventory accordingly. Amazon is using machine learning to optimize its delivery trucks and routing. And Intel is using machine learning to predict when parts will fail in its factories.Machine learning can be used to predict future demand for products, optimize stock levels, and route deliveries in the most efficient way possible. There are many commercial applications of machine learning for inventory management and logistics. For example, online retailer Amazon uses machine learning algorithms to predict customer demand and optimize stock levels. Retailers can also use machine learning to forecast inventory needs for the upcoming season.

Olivia Tan, who is a tech expert and a Co-founder of Cocofax, says,

Machine Learning is applied in Inventory management and logistics: Companies are now dependent mostly on ML for their inventory management. For tracking products, bookkeeping, ships, transports, delivery etc are done by the ML accurately. Thus companies have reduced wastage and have been able to maintain time.

Robot in repetitive tasks: Time and money are being saved by companies as repetitive tasks have been easy to handle using robots. This is the product of ML. Hardware and softwares are used in several fields like data entry, sorting, or manufacturing.

Prediction: ML can predict the future demand of a product. Because it sorts past and present data to determine the upcoming demand, this prediction has the most accuracy rate among all methods of prediction. Also, the manufacturing process is controlled according to the prediction. So companies can save themselves from deadstock.

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Machine Learning for the automated business process:

Machine learning algorithms can automatically improve given more data. Machine learning is mostly used for business automation. It can be used to predict outcomes, forecast demand, personalize customer experiences, and automate business processes. The potential applications are endless.

Machine learning can automate tasks that would otherwise require human input, saving businesses time and money. Some of the ways businesses are currently using machine learning include:

Automatic categorization and tagging of images and videos.

Images and videos are a large part of the content on the internet. With so much content being uploaded every day, it is important to have a way to automatically categorize and tag them so that users can find what they are looking for. This is where machine learning comes in. In the context of image and video recognition, machine learning can be used to automatically identify objects, scenes, and activities in images and videos. This can be done by training the algorithm using a set of images and videos that have been manually categorized and tagged. Once the algorithm has been trained, it can be used to automatically categorize and tag new images and videos. This can be used for a variety of purposes, such as automatic categorization and tagging of images and videos, or identifying and tracking objects in videos for security purposes.

Fraud detection

One of the most important applications for machine learning is in the detection of fraudulent activity. In the context of online fraud detection, machine learning can be used to identify patterns in fraudulent behavior that would be difficult for humans to detect. Machine learning algorithms can be trained to recognize specific indicators of fraudulent activity, such as the use of fake or stolen identities, the creation of fake accounts, or the submission of bogus orders. Machine learning can be used to detect patterns in data that are indicative of fraudulent behavior. Once these patterns have been identified, the algorithm can be used to automatically detect fraudulent behavior. This allows businesses to take proactive measures to prevent fraud from occurring. Machine learning is very effective in the detection of fraud. In a study by IBM, it was found that machine learning was able to detect fraudulent activity with a 90% accuracy rate, while humans were only able to achieve an accuracy. One of the most important applications for machine learning is in the detection of fraudulent activity. In the context of online fraud detection, machine learning can be used to identify patterns in fraudulent behavior that would be difficult for humans to detect. Machine learning algorithms can be trained to recognize specific indicators of fraudulent activity, such as the use of fake or stolen identities, the creation of fake accounts, or the submission of bogus orders. Machine learning can be used to detect patterns in data that are indicative of fraudulent behavior. Once these patterns have been identified, the algorithm can be used to automatically detect fraudulent behavior. This allows businesses to take proactive measures to prevent fraud from occurring. Machine learning is very effective in the detection of fraud. In a study by IBM, it was found that machine learning was able to detect fraudulent activity with a 90% accuracy rate, while humans were only able to achieve an accuracy.

Predicting consumer behavior

Machine learning can be used to predict consumer behavior by analyzing past data about customer interactions. This data can include information about what products customers have purchased in the past, what websites they have visited, and how they have interacted with the company’s website or social media pages. It can be used to figure out what people are likely to buy, what they are likely to click on, and how they are likely to respond to marketing efforts. Machine learning can also be used to predict how people will behave on social media. This can be used to figure out what kind of content is likely to go viral, what time of day is best for posting, and what kinds of posts are most likely to get shared. Machine learning is a powerful tool for predicting consumer behavior, and it is likely to become even more popular than the present.

Speech recognition

A machine learning algorithm is a computer program that can learn how to perform a task, by adjusting its own parameters, using a training dataset of example inputs and desired outputs. The task might be something as simple as recognizing individual words, or as complex as understanding natural language. Speech recognition is an application of machine learning that enables a computer to understand human speech. Machine learning has been used in speech recognition for many years. The first speech recognition system was developed in the early 1960s. However, the performance of these systems was not very good. In the early 1990s, machine learning was used to improve the performance of speech recognition systems.

Image Recognition

In the context of image recognition, machine learning algorithms can be used to automatically identify and label objects in images. This can be used for various purposes, like tagging images on social media or identifying objects in video footage for security purposes. Machine learning algorithms can be trained using a variety of data, including images, textual data, and audio data. To achieve good results, it is important to use a large amount of data that is representative of the problem domain. The use of machine learning for image recognition has become increasingly popular in recent years due to the rapid growth of digital image data. The availability of large datasets and powerful computing resources has made it possible to train machine learning algorithms to achieve high levels.

LyleFlorez the founder of EasyPeopleSearch says,

Machine learning is improving business functions and automates business processes for various organizations and industries Its application is wide and versatile and includes:

Human Decision Analysis

Machine learning has its applications in decision support for humans, especially when faced with the best course of action to take. It uses various algorithms of past events or any other data to run through possible scenarios and provide solutions on the best course of action to take.

Customized Recommendation

Machine learning has deep roots in learning about the preferences of each customer and tailoring them according to individual choices. It uses a set of algorithms that processes specific data about an individual, ranging from past purchases and inventory to know what best to recommend to each customer.

Unique Pricing Algorithm

Companies and industries can apply machine learning to help tailor their pricing strategy and know how much to tag their products. Machine learning employs data from previous pricing, as well as other notable factors such as demand, weather and time that will influence its price.

Phishing Detection

In its course, to provide a safe environment for business transactions, machine learning has employed algorithms for detecting suspicious and fraudulent activities by detecting anomalies that function differently from the established patterns.

Machine Learning in Cybersecurity

Machine learning has been used in many industries to improve the efficiency and effectiveness of processes, including cybersecurity. Machine learning can be used to identify malware and malicious activity that would otherwise go undetected. Machine learning algorithms can be used to identify malicious activity, including malware, ransomware, and phishing attempts. They can also be used to create models of normal user behavior so that any deviations from the norm can be detected and investigated. Machine learning is a powerful tool for cybersecurity and is constantly evolving to keep up with the latest threats. Machine learning is a powerful tool for cybersecurity, and its use is only going to increase in the future.

Ouriel Lemmel, CEO and founder of WinIt, shares my thoughts on the latest applications of Machine Learning.

*Machine Learning can be used for Computer Security and defend networks from attack. *

New threats to cybersecurity appear every day, and human workers can have a hard time keeping up. But Machine Learning offers powerful tools to help keep computers safe from attack. Machine Learning has proven to be excellent at finding trends in data, and we can train a Machine Learning algorithm to identify attack vectors and alert human workers to them before they do any harm.

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Conclusion:

Machine learning is being used in more and more applications every day. This field of study is growing rapidly, and there are many opportunities to pursue a career in machine learning. If you are interested in this exciting and rapidly-growing field, consider pursuing a machine learning course. When you enroll in the Machine Learning course, you’ll learn the basics of machine learning, including how to develop and apply models to real-world problems and also become job ready. So what are you waiting for? Enroll now and start your journey into this exciting field!

Machine Learning has clearly made an entry into our lives and is here to stay. Its applications are no longer limited to enterprise use. ML programs and algorithms have evolved over time and taken over most industries to improve consumer experiences. If you wish to know more about the domain, check out Great Learning’s M.Tech in Data Science and Machine Learning Course.