Tag Archives: Machine Learning

Machine Learning

5 Unexplored Benefits of Machine Learning in Retail

When we start examining how technology is changing life, education, and the business landscape as a whole, we might not be able to exhaust everything in a day. Technology is now a mode of life and there is no denying that. Among the top-notch technologies conquering every aspect of life, we have Machine Learning (ML) and Artificial Intelligence (AI).

Machine learning alone is a subset of artificial intelligence and deals with the use of data and algorithms to gain insights and draw predictions from the raw data input. Precisely, ML is a technology that allows systems to learn on their own i.e from experiences without being explicitly programmed.

The growth of data science has led many businesses and companies to leverage technologies like ML and AI. With such technologies in place, business processes are streamlined, and business owners are able to attain and digest big data and make more informed decisions. When computers are fed with data; whether from customers or business owners, they are able to learn and update their knowledge base continually.

Machine learning is revolutionizing eCommerce and retail as a whole by allowing retailers to stay ahead of the competition, forecast market changes, and provide better customer service. Applying machine learning to business workflow helps to achieve optimal efficiency, eliminates human error, and results in the better utilization of business data. When it comes to marketing, ML helps in the process of analyzing large data to gain insights into customer behavior, spending, and customer lifetime value. It also leads to better-targeted marketing campaigns.

The global market for machine learning was valued at USD 15.44 billion in 2021. More insights show that the ML market is expected to grow to USD 209.91 billion in 2029. Other findings indicate a 300% increase in investments in machine learning and AI technologies. 91.5% of the world’s companies besides Amazon, Alibaba, eBay, Netflix, and Walmart are already investing in machine learning, automation, and artificial intelligence.

Regardless of the industry, or type of business (online or brick-and-mortar), any retailer can leverage ML technology to improve marketing strategies, increase sales, or double ROI. However, it’s crucial to first understand how machine learning works to bring about optimal results. Now let’s explore more about the benefits of Machine Learning in retail.

5 Unexplained Benefits of Machine Learning in the Retail Sector

  1. Personalized Product Recommendations

This is the age of personalization and the trend isn’t about to fade away. A report by McKinsey & Company shows that customers not only want personalization but demand it. The report also shows that companies that manage to connect with their customers generate faster revenue. To substantiate, 71% of customers expect personalization from their brands. Todd Yellin, Netflix’s vice president of product innovation, describes personalization as the process of creating the right connection between the viewer and the content. In addition, Netflix managed to save $1 billion due to its machine learning algorithm that personalizes content recommendations for its users.

Personalized product recommendation results in better customer experiences and consumers are left more satisfied than without it. Many brands and businesses are aware of how valuable every customer is. To enhance experiences, generate leads, and boost sales, companies leverage ML to understand customers’ behavior depending on purchase history or profile. The technology studies customers’ or shoppers’ data input or queries and then begins cultivating experiences based on the data input.

With time, the website automatically brings up related searches and similar products based on historical data or what other customers searched about. Retail companies use recommendation systems to help customers access the latest content and personalized offers they would benefit from. These efforts increase sales and improve buyer journeys.

  1. Optimized Supply Chain Management

Market dynamics are forces that every business must deal with. However, with machine learning technology, businesses can have their supply chains optimized to eliminate or reduce disruptions and meet customer demands on time. Machine learning provides an avenue where large volumes of real-time data are centralized, accessed, and used to make decisions.

Retailers get to know how to better manage their inventory or look for other suppliers to achieve a balance between the stock in hand and the customer demand. Businesses can also stock up on goods or resources once they predict a shortage in the near future. Precisely, ML allows automated quality inspections and reduction in forecast errors due to its efficiency.

For example, an apparel store can leverage machine learning to study customer behavior, analyze its current stock, and plan for procurement. The retailer is able to learn about future customer behaviors and then stock more where necessary. ML impacts every point of supply chain management from procurement, and logistics, to stock availability.    

  1. Forecasting Future Trends 

Modern markets are fast-paced and forecasting is key to predicting future market changes and why they will occur. With the advent of COVID-19, a drastic change in customer behavior, and supply chain disruptions, it has become more significant for retailers to rely on data to understand future market trends.

Market forecasting is the process of analyzing the current trends to predict future trends. Machine learning helps retailers to predict market patterns by assessing current customer behavior, purchasing power, and preferences among other forces. Machine learning has become crucial in forecasting future trends by providing authentic data to retailers.

Computer algorithms recognize people’s genders, ages, preferences, and purchasing power. It helps retailers to do away with guesswork, most especially when it comes to planning and decision making. They are able to make realistic predictions that have a far-reaching impact on business ROI and growth. Retailers are also able to create optimized and targeted marketing campaigns to increase customer lifetime value. All in all, trend forecasting helps retailers stock up on products that will be in demand or make better marketing decisions.

  1. Greater Efficiency in Finance & Payments

The recent developments are paving the way to harness machine learning in financial management and payments. With the help of ML and AI, retailers can track customers’ purchase trends through various channels like CRM databases, user-generated content, and purchase history. As people prefer online payments, and order discounts, machine learning helps in price optimization and fraud detection.

Machine learning involves data analytics and it can help in studying customer and purchase behaviors. In case there are sudden changes, there is better predictability as algorithms can revise the data. Businesses can then set better Key Performance Indicators (KPIs) to impact the business tasks and their bottom line.

Utilizing machine learning in payments and financial management will require a retailer to first find an eligible team with professional and technical experience in several areas like data, computer science, and advanced analytics.

These should work hand in hand with the sales, marketing, and management teams. Machine learning currently caters to credit card transaction monitoring and this helps in the real-time authorization of transactions and elimination of errors in payments.

Besides, when recurring payments or invoice operations get automated, it will add to positive employee experience leading to greater workplace motivation. Employees can then focus on other imperative tasks without having to worry about monotonous and repetitive tasks.

  1. Seamless Customer Experiences  

It is every business’s target to get more leads, and sales, and offer the best-in-class customer experiences. Before, retailers relied on human efforts to offer the best experiences to customers. However, with the advent of top-notch technologies like AI, ML, and data science, retailers can rely on facts to craft better experiences depending on customer habits and interests.

eCommerce and retail are all about experiences and with ML, retailers can optimize buyer journeys through personalized shopping experiences. Machine learning helps businesses gain insights into raw data such as customer purchasing habits and trends and are able to understand demographics, improve browsing experiences or invest in targeted marketing campaigns. Algorithms are more accurate in identifying what customers are more likely to buy more in the future and retailers are able to direct their marketing efforts to the right consumers and markets.

To encapsulate, Machine learning (ML) and Artificial Intelligence (AI), are fascinating technologies stirring up every aspect of life. From recommendation engines, and self-driving cars to seamless voice searches, ML and AI are revolutionary in every way. In retail, ML is undeniably an influential factor in understanding customer data, crafting marketing campaigns, and providing seamless CX. As a retailer, it’s essential to implement AI and ML technologies to optimize marketing efforts, business growth, and ROI.    

Author Bio:

“Doing what you love is the cornerstone of having abundance in your life.” Wayne Dyer’s thoughts are well suited to Kiara Miller. She has been working as a content marketing professional at ‘The Speakingnerd’. Her passion for writing is also visible in the innovative joys of material she provides to her readers.

Machine Learning

Machine Learning in Healthcare: Benefits and Top used cases

Most emerging technologies like Machine learning (ML) and Artificial Intelligence (AI) are going hand in hand to bring the revolution in the healthcare industry.

ML is the sub-category of Artificial Intelligence (AI) that focuses on improving the performance of the medical team constructively with less time and more speed using large-scale medical data.

Undeniably, the Healthcare industry has always been open to adopting new technologies. From Big data tools and data analytics to EMR/EHR, the healthcare industry has always been a strong supporter of innovative new technological advancements.

The ML and AI technologies are efficient in handling complex and large amounts of data. The automated ML system provides high-quality healthcare services and facilities to the patients.

So, let’s explore machine learning’s benefits.

What are the benefits of Machine learning in healthcare?

The use of machine learning in healthcare has proved very beneficial.

  • Large amounts of data handling, processing, report generating, diagnosis and health solution options are all important tasks that are effectively handled by the ML system with much ease and security.
  • It enables healthcare professionals to save time so that they can focus on the other key activities related to patient care solutions.
  • The data and the algorithm makes various patterns which are detected by the computer through ML in order to predict care outcomes and adequate diagnosis strategy
  • ML performs the same tasks more efficiently than the manual way which saves ample time for the medical professionals
  • The ML trains computers to interpret the data and provide the solutions automatically
  • Various conditions and points allow ML to interpret data in the administered or unadministered conditions
  • Machine Learning Helps in patients’ involvement in the process like the physical presence of the patient during the treatment
  • Regular health concerned tips or messages are sent to the patient to monitor the health conditions which saves time for the patients

What are the top machine learning use cases in healthcare?

Therapeutic Imaging

Magnetic Resonance (MR) and Computed Tomography (CT) are the processes used in therapeutic image recognition. It is the process for image analysis, disease detection and disease prediction.

The clear specification is made by the study of a combination of image data such as calculation of the tissue size, volume and shape.

The algorithm is structured to make early detection of the diabetic possibilities, early stage of Alzheimer and ultrasound detection of the breast clots. Furthermore, the radiology and pathology areas are also detected seamlessly.

The convolutional neural networks (CNNs) allow the medical experts to simplify the complex data analysis and rectify the health issues of the patients with immediate effect and with conventional accuracy.

The reports have proved that CNN’s detection of Atopic dermatitis (eczema) is the disease in dermatology images with more than 15% accuracy than the physical examination by the experts.

Malignant rate

The development of the tumour can be detected. The size of the tumour can be tracked and the phases of tumour development which can go unnoticed can be reported accurately by the increase in the data given by the algorithm with more CT and MRI scans.

The algorithm gives such accurate readings that there is no possibility of missing out on any tumorous cell, every detail information like how fast and in what range the tumour is growing in the body, all such data is accurately captured.

Oncology and skin specialists demand the use of algorithm detection of chronic diseases as they support and guide medical experts with early detection of the disease. Many lives can thus be saved with the machine learning technology, suggests the experts.

Data Analytics in the healthcare

Machine learning can analyze the Electronic health records (EMR) that contain huge structured and unstructured data, clinical reports, medication tracks, diagnoses and lab reports with phenomenal speed and accuracy.

Wearable devices and also smartphones are supported by mobile application programs allowing the user to track their health records and get accurate readings and reports.

The device can successfully measure the pulse rate, body temperature, respiration, mobility of the patient, heart rate, Blood pressure reading etc.

Patient Churn Analysis

The churn analysis is the matrix that keeps track of the patients’ activities and the number of visits to any particular hospital. The data collected from the source about the patient’s visit to the doctor gives accurate feedback.

The prediction is done by the analysis report on whether or not the patient will revisit the same doctor in future. The possibilities of the patients who won’t visit the same doctor again are studied and measures are taken to develop opportunities that can pull back the patients for future visits.

Research and Development

The new technology is advancing the discovery and enhancement of drugs.

The genomic, clinical and population data is processed to detect the viable drug by the algorithms of the system. The large data is studied in detail by the pharmaceutical companies for their future business growth.

Introduce new anomalies

Machine learning trains the computer to identify the patterns with the help of the data and the algorithm to understand the data. This process of learning from the data is called training and the result we achieve is the model. The models used in this technique are classified.

The main objective of this classification is to tag the data. The clustered data is the undefined or unlabelled data where we don’t have any outcome from the data which is widely in the collection.

With the study of distinctive patterns, the regressive data is collected in order to check whether the patient with chronic disease will come back again for the treatment or not.

While obtaining all the labelled or unlabeled data information, we come across anomalies. Through the statistical techniques, all the patterns of the data are analyzed and the unrequired data bites are separated which identifies the fraud activities in the various clinical operations.

All unstructured and unlabelled data is removed so it doesn’t affect the model in any way. Several statistical techniques help to tackle the new anomalies.

Conclusion

Autonomous robotic surgeries and personalized medical assistance with ML have supported greatly in fighting against diseases and getting back to life with a much higher spirit and energy.

The confidence is gained back by the patient, and technology has widely changed the scene on chronic diseases as well. With proper diagnosis and treatment, the deadliest disease can be cured with the right treatment.

Robots are performing surgeries on humans with a high success rate. Soon we can also imagine having the healthcare software development companies introducing applications to our phones which can identify the symptoms and guide us for future treatment.



Machine Learning

5 Top AI and Machine Learning Trends for 2022

Artificial intelligence and machine learning are two of the most talked-about technologies in recent years. They are changing the way we live and work and will continue to do so in the future. These technologies will only become more prevalent in the future. They have the potential to change our lifestyle and many businesses are already taking advantage of them.

AI is a process of programming machines to make decisions for themselves, while ML is a method of teaching computers to learn on their own by analyzing data. Both fields have been around for many years, but they are only now becoming more popular due to recent advances in technology. These technologies are making changes in industries across the board, and businesses that don’t adopt them will be left behind.

The future of AI and machine learning is looking bright. These technologies are making an impact in a variety of industries, from healthcare to finance to manufacturing. These technologies are making it possible for machines to learn on their own, and to make decisions based on data. In this blog, you will read about the top Artificial Intelligence and Machine learning trends in 2022.

Artificial Intelligence and Machine Learning Trends for 2022

There are a number of trends that we can expect to see as AI and ML continue to develop in 2022. Here we will take a look at some of the trends that we can expect to see in the future when it comes to AI and ML:

  1. Improved Cybersecurity

As we move into 2022, it is clear that artificial intelligence (AI) and machine learning (ML) will be having a major impact on cybersecurity. Both of these technologies have already begun to play a role in improving security, and their influence is only going to grow in the coming years. These new technologies have the potential to improve security in a number of ways, including by increasing the speed at which threats can be detected and stopped. Cybercriminals are beginning to leverage AI and machine learning themselves in order to launch more sophisticated and difficult-to-detect attacks, so it is essential that businesses have security measures in place that can counter these threats.

AI in cybersecurity can help automate the process of identifying and stopping cyberattacks before they cause serious damage. With the help of AI in cybersecurity, these threats can be mitigated and businesses can continue to enjoy the benefits of these transformative technologies safely and securely. Today, it is more important than ever for businesses to invest in AI-enabled cybersecurity solutions. These threats can be mitigated with the help of AI in cybersecurity solutions, but only if businesses are aware of these dangers and take appropriate precautions.

  1. AI in Metaverse

 Metaverse, a distributed ledger platform and virtual world are known for their innovative applications of blockchain technology. In recent years, AI has been making waves in the Metaverse community. Metaverse is a digital world that enables people to create their own avatars and interact with others online. In 2022, AI will play a huge role in Metaverse, and here are four ways it will change the game:

  • Avatars will be more realistic and lifelike
  • Virtual reality will become even more immersive
  • There will be more opportunities for businesses to use AI in Metaverse for marketing and customer service purposes
  • The overall experience of being in Metaverse will be greatly improved by AI
  • Bots will become more common and smarter, automating many tasks for users
  • Avatars will be able to create a more personalized experience for each user
  • AI-powered marketing and sales bots will help businesses boost their revenue significantly

  In Metaverse, users can create their own avatars, interact with other users, and explore different virtual environments. The development of quantum computers will enable new possibilities for AI in Metaverse, such as real-time translation and realistic rendering of objects. However, we should still wait to know more about the future of work and society in the metaverse.

  1. AI and ML with the Internet of Things (IoT)

The Internet of Things (IoT) is growing rapidly, and by 2022 it is expected to include more than 30 billion devices. With this many devices connected to the internet, it’s no wonder that AI and ML are becoming more important. These technologies will be necessary to manage and process all of the data that will be streaming in from all of those devices.

The internet of things (IoT) is a network of physical objects, vehicles, buildings and other items—embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data. In 2022, AI and ML will be used with the IoT to create more efficient systems. For example, hospitals will be able to use AI and ML to predict patient needs and better manage resources. Traffic lights will be able to adjust their timing based on the current traffic conditions in order to optimize traffic flow. Buildings will be able to adjust heating/cooling based on occupancy levels and time of day. There will be many more applications of AI and ML in the future. If you know how to master Machine Learning, you can easily be a part of several innovations in the future.

  1. Use of AI for Environmental Threats

 AI and its potential applications for environmental threats were outlined as one of the top trends to look out for in 2022. The report, compiled by analysts, cited concerns over air pollution, water scarcity, and climate change among the major factors that will drive the growth of AI-based solutions in the next few years. As the world becomes increasingly digitized, more and more data is being generated about our environment. This data can be used to create models that predict environmental changes and help us respond to these changes before they become a problem.

With global temperatures on the rise and more natural disasters occurring each year, businesses and governments are starting to realize just how important it is to have systems in place that can help predict and prevent these threats. AI and its potential applications for environmental threats can be used to monitor air quality and track emissions. It can also be used to identify areas that are at risk for pollution and develop strategies to prevent it.

  1. Hyper automation in Healthcare

The use of AI for hyper-automation in healthcare is on the rise. What is hyper-automation in healthcare? It is the application of artificial intelligence and machine learning technologies to automate complex tasks and processes in the medical field. This can include anything from diagnostics to treatment plans. Many experts believe that hyper-automation will play a major role in the future of healthcare.

Many experts believe that hyper-automation will play a key role in reducing costs and improving patient outcomes. Simply put, hyper-automation is the application of AI and automation technologies to healthcare where they can drive significant improvements in efficiency and quality.

To Conclude

The world of AI and machine learning is constantly evolving. Every day, new technologies are emerging that are changing the way businesses operate. As we move into 2022, there are a few trends that we can expect to see more of.  Let’s wait and see what AI and ML have more to bring in 2022!

Author Bio:

Shahista Tabassum is a senior IT Technical Trainer at Time Training Center.  She has extensive work experience of 11 years working in various roles as a software developer, It Consultant and Technical Trainer. She spends her free time learning new things that will enhance productivity and in volunteering activities that help kids to learn new things.

 

Machine Learning

Machine Learning – Application Examples

The capability of computer systems to know and understand without being explicitly programmed, is how machine learning is defined. It is a form of Artificial Intelligence.A complex computer program has the ability to change, improve and learn, without being told what to do. A machine can thus learn by itself, without a programmer having to put rules or parameters into the program.

This enables the computer to make useful decisions and act in ways that would not have been possible without having a pre-programmed instruction.Machine Learning allows computers to take a ‘new-found understanding of the world, and change the way that they understand the world.

You’ll find applications of machine learning and deep learning techniques that go way beyond what you could achieve with a simple “voice recognition” or “predictive chat” service.With current computer hardware, artificial neural networks can be trained with minimal resources on sparse, low-quality data. We will look at applications in an effort to discover the potential of such algorithms.

With machine learning you can build intelligent systems that act like a human mind, and in many cases the systems will be able to do more and more tasks much faster and more accurately.Machine learning is very useful for many applications.

These are all example applications that are run on computers. And today those systems are limited in the accuracy they can achieve. But there are techniques to achieve better performance and look at how this may be applied to areas such as e-commerce, healthcare, education, robotics and so on.

While machine learning is an interdisciplinary area, one area of application is computer vision, and there’s plenty of hype about the application of this technology in augmented reality (AR) development.The learning of machine learning is in the algorithm design. For example, you can use one method to optimize a goal or map out a journey.

There are hundreds of apps that utilize Machine Learning every day. Let’s see some examples.

Financial services Google suggests the best investment products based on your personal life, including, based on your travel history, the price you paid for a flight and even your preferred option. This allows you to save up to 20% annually!

Facebook’s face detection and recognition technology, which is based on superior deep learning , Automatic friend tagging suggestion by Facebook is a very common application of Facebook that you must have come across while crawling through your Facebook page whenever a picture is uploaded on Facebook, it suggests you asking if you want to tag your friend in the picture or not.

online Ads recommendation and machine learning. Have you ever considered the significance of those advertisements in your search history. For example you want to buy a product on Amazon, but you forgot after sending it to the shopping cart. When you wake up the next day. you are browsing YouTube for some tutorial, you will be astounded to see an advertisement for the same product that you searched on. Because of you Google keeps track of your search history. The ad is then recommended based on that.

The world’s first robotic kitchen Moley will recreate any dish cooked by any massive chef in the world. Moley is a pair of fully articulated robotic hands that can replicate the whole function of human hands with the same speed and sensitivity. Moley kitchen works based upon the machine learning algorithm protocols.

Uber uses machine learning techniques to bring greater personalization into its app. The app starts simply by  asking for your destination and includes a number of predictions based on your habits and current location. For example, if you are at work, it will assume that you want to go home, to the gym, or to the pub. You could say that it is capable of understanding and guessing your travel habits.

For UberEATS uber’s machine learning platform Michelangelo executes numerous algorithms to find meal delivery time forecasts, search rankings, and restaurant rankings. Before an order is placed, the delivery time models anticipate how long it will take to cook and serve a meal, as well as at each stage of the delivery process.

There are numerous applications that are working on artificial intelligence and machine learning technology.

Machine Learning

How is Machine Learning Making The World A Better Place?

“Machine learning has enormous advantages and applications—it is capable of making the world a better place—even it can destroy it in no time. Everything depends on how you take it and apply it to the sole purpose of humanity and others’ life. ”  

Introduction

The world is already a better place not only for humanity but for everyone around. But what creates a big difference is machine learning and its application that shows new ways to live a life of many dreams coming true. The world has become fast-paced, and competitions are too high. Surviving is not the key here but innovative, following the latest trends and automation to sustain any business.

And if you see the latest machine learning applications, you can witness how the world shapes into a better place—and how you can make it easier either by creating or following the norms.

Let’s dive in and explore how machine learning plays a significant role in making the earth a better place to live.

Machine learning Applications in Covid-19

Humanity would have been extinct, uncountable dead bodies lying here and there if there would have been no machine learning. The Covid-19 virus is that dangerous. After the 1st and 2nd waves, the third wave will hit in the next few months.

Imagine what would have been the case if there were no facilities to track daily records of newly infected, death, and recovered rates. Consider the ease of monitoring vaccinations, doses and scheduling them with Arogya Setu from your mobile device.

And when you open the app, it asks a few basic questions to know you better about your state and with whom you have come in contact using simply Bluetooth and GPS. Even it gives you regular information about the person you have come in contact with and asks you to take necessary precautions before Covid-19 attacks you and you become the next super spreader.

You only see the information on the news channel or the website. Has this question ever popped into your head “how is everything trackable, and can we know the exact situation of Covid cases?” It’s all feasible because of machine learning algorithms and their suitable applications by data scientists and machine learning engineers.

Every time you take an RT-PCR test, the results land at your registered mobile number within 24 hours. Not only you, but 10 -15 lakhs tests happen a day, then how is the government able to track everything and save individuals from deadly Covid-19—that’s the power of machine learning towards healthy humanity and the earth. 

Machine Learning Applications in Education

Education leads to wisdom, being innovative and practical at the same time. But if you lack one, you suffer for a lifetime. It’s as simple as that to sustain in this highly competitive world.

Machine learning played a crucial role in scaling up the educational system. Today it is a whole new level than it was years back. Using the LMS allows busy professionals to learn at their convenience while scaling up their careers.

But due to the worst Covid-19 hit, schools and other educational institutes somehow managed to bring their whole educational system into the track, launching their LMS platforms and live classes.  

The real magic with the LMS platform is: it tracks each data of each person. Lecturers won’t have to teach again what you missed, but you can attend them online till you grasp the core concept of it. It also tracks how many modules are left and that you have completed. Even it gives the lecturers the freedom to collect the assignment and share their reviews just like it happens in the classroom.

Banking and Financial Industry To Safeguard Your Financial Activities

Imagine for a moment what if all the ATMs near you stop working for days, can’t dispense cash for you, and UPI and internet banking go on hold for an unlimited time due to server issues. Then how would you see the world now? Going crazy, right? Even the bank staff will fall crazy, and so their computers handle the whole people in their areas.

But to manage hassle-free and keep track of every transaction, machine learning plays a vital role in the banking industry. It tracks the loans, cash, balances, cheque, UPI transactions from third-party apps, credit and debit cards, internet banking, etc. When there is a withdrawal or deposit, it sends a text message to the registered number about the latest balance.

When finance is involved, there are risks, data tampering, unauthorized access, fraudulent activities. Still, machine learning has self-learning activities that notify the users and the respective bank at the same time when there is withdrawal. Even with OTP, the two-factor verification adds an extra layer of security to prevent unauthorized access and data tampering and keep you safe from money laundering activities.

Online Recommendation Engines For Getting You Your Favorite Stuff

Ever came across this question, ‘Why is the eCommerce business one of the most successful business models globally, and why are more and more entrepreneurs into this model lately?’ Yes, e-Commerce is the futuristic business model for which most businesses are in threat.

The secret to their success is that more and more entrepreneurs are the eCommerce business-savvy recommendation. When you buy an iPhone or click on the ‘add to cart option’ the next moment, it shows you ‘people who have also brought these items’ with a cover and tempered glass. This way, it persuades you to buy more items at a time with some handy discounts.

Even online platforms like Netflix, Amazon Prime Videos, Hotstar+Disney, youTube, and Spotify use an innovative engine recommendation system based on experience to recommend the next watch. Every time you watch, it tracks the data about how much time you spent watching, how much is left, so when you open it, it shows you the currently watching, top ten grossing based on most people watching, and so on.

Google Search Algorithms & Personal Assistants As Helping Hand For All Problems

Google is a repository of uncountable data that has limitless potential. That’s why the whole world runs and trusts Google for all the required information and answers. Google uses NLP to understand human queries and personal assistants ( Google Voice Assistant, Amazon Alexa, Apple Siri) to give them the best possible solutions.

Whenever a person searches using voice assistants or search engines, these ML algorithms behind the search engines break the whole queries into small chunks using NLP, look for the exact information, and the most relevant one that people have visited the most.

But the fundamental transformation that happened when personal assistants got into the real action was the best thing that happened to humanity. Take Google, for example. Say ‘Ok Google!’ to activate your assistance and ask your queries without typing, or ask Google to send a message, dial someone. Or play your favorite songs, book a table for the next meeting or remind you about the birthday; Google does everything flawlessly.

It needs your voice and can read news for you, say about the forecast, the traffic, whatever you want from Google—ask away. The same applies to other search engines and personal assistants. And guess what, you all have them on your mobile devices.

Email Spam and Malware Filters To Save You From Online Frauds

Online frauds are the most common thing in today’s world, and cyber attackers use email to target their new potential customers. They use lottery, money, and other things to steal your credit card information and ask you to share your OTP to use your data against yourself.

So with updating algorithms, whenever a mail lands in your inbox, Google uses various filters to decide where it is supposed to land. If it contains a few words, spam, or many users marked spam to the senders, Google filters learn it using ML algorithms. It automatically sends that email to the spam folder to protect you from different fraudulent activities and online phishing.

Though it’s not a new thing already, it’s getting robust with time, now more trustable with safe keeping from online frauds.

Final Words

These are a few top-notch applications of machine learning to make the world a better place to live and give us a healthy lifestyle. Yet, many applications are breathtaking. Machine learning has essential applications for protecting human life, as evidenced by the recent Covid-19.

The applications of machine learning made the distance between two places almost negligible today. You can go on video calls to meet your favorite persons virtually, even attend meetings virtually over zoom calls and Microsoft team meetings that add an extra layer of data security to protect your privacy.

So use machine learning in the right way; it has the potential to get you the best things you have ever imagined. It has life-changing applications in many ways, so you can make the world even better, a happier place than it is now.