35 Applications of Machine Learning (ML) and Use Cases in Daily Life

Hi Learner! Today, here we will explain you in detail about various applications of machine learning as well as uses of machine learning across different industries with ease. After reading this article, you will be getting fully educated about real world applications of machine learning without any hindrance.

About Machine Learning

Machine learning is a subset of the Artificial Intelligence (AI) that serves the machines along with the capacity to automatically learn from data and past experience by getting to identify patterns to create the predictions for new processed with less human being intervention. Machine learning helps to the rescue in many circumstances where it is not possible to apply the strict algorithms.


When merged with the deep learning, computer vision, neural networks, and big data; then machine learning has amazing potential to transform every sector and increase the customer experience.

Machine Learning Applications Tutorial Headlines:

In this section, we will show you all headlines about this entire article; you can check them as your choice; below shown all:

  1. About Machine Learning
  2. Applications of Machine Learning in Real Life
  3. Uses of Machine Learning
  4. FAQs (Frequently Asked Questions)
  • What are the applications of machine learning in computer vision?
  • What are the applications of machine learning in healthcare?
  • What industries use machine learning the most?
  • What are the applications of machine learning in drug discovery and development?
  • What are the applications of machine learning in mechanical engineering?
  • What are the applications of machine learning in cancer prediction and prognosis?
  • What are the applications of machine learning in finance?
  • What are the business applications of machine learning?
  • What are the top 10 uses of machine learning in real-life?
  • What are the main uses of machine learning in daily-life?

Let’s Get Started!!

Applications of Machine Learning in Real Life

Here, we are going to reveal several real world applications of machine learning across various industries; below shown each one, you can check them:

Also Read: 20 Examples of Machine Learning in Real Life!!

  • Predictions While Commuting
  • Virtual Personal Assistants
  • Sentiment Analysis
  • Videos Surveillance
  • Marine Wildlife Preservation
  • Image Recognition
  • Predict Potential Heart Failure
  • Social Media Services
  • Online Fraud Detection
  • Traffic Alerts By Using Google Map
  • Most Intelligent Gaming
  • Email Spam & Malware Filtering
  • Creative Arts
  • Online Customer Support
  • Real-Time Dynamic Pricing
  • Speech Recognition
  • Search Engine Result Refining
  • Extraction
  • Get Automation as Employee Access Control
  • Statistical Arbitrage
  • Product Recommendations
  • Self-Driving Cars & Automated Transportation
  • Stock Market Trading
  • Adopting for Dangerous Tasks
  • Banking and Financial
  • Enhanced for ElderCare Experience
  • Cyber Security
  • Agriculture Sector
  • Environment Protection
  • Ads Recommendation
  • Automatic Language Translation
  • Auto-Friend Tagging Suggestion
  • Healthcare & Medical Diagnosis

Uses of Machine Learning

In this section, we are going to explore all possible applications areas, where are going to use of machine learning in different sectors; below mentioned each one in detail, you can read them:

Also Read: What is Expert System in AI? Applications, Examples, Types, & Uses!!

Predictions While Commuting:

Most of people are using the GPS navigation services. By helping of this, our real time location and velocities are being stored at the central server for handling the traffic. This data can be used to make a map of the current traffic; as well as it also assists for preventing the traffic and congestion analysis; but there are only some vehicles, which are enabled with GPS feature. Hence, the machine learning concepts in that case helps to estimate the spots where congestion can be easily searched at the basis of daily experience. Whenever, you want to book the cab, then app offers the estimation the rate of the ride.

Virtual Personal Assistants:

Today, we are going to use many virtual personal assistance like as Alexa, Cortona, Siri, and Google assistance. With helping of them, user can find the any information by using own voice instruction. These assistance lets users help to use in many ways only by own voice instructions like as call to someone, open the email, play music, scheduling an appointment and more. This assistance records our sound instruction, move it to server over the cloud and decode it by helping machine learning algorithms and perform its tasks perfectly.

Sentiment Analysis:

Sentiment analysis is also one of the very most mandatory applications of machine learning. It is a real-time ML application that helps to identify the opinion or emotion of the speaker otherwise writer. For example, when anyone has written a review or email then sentiment analyser will get quickly searching out the perfect thought and tone of the text. This is widely going to use to analyse a review based website, decision-making application and so on.

Videos Surveillance:

The video surveillance is the most eminent example of the machine learning that helps to detect any offence before it occurs. It is the most sufficient as compare to observe by the human, because it is more difficult and boring work for the human to keep monitoring on several video at once. Then, video surveillance is most helpful as they keep monitor on certain behaviour of the person such as keep standing motionless for a longer time duration, napping on benches, stumbling, and so on. If, the video surveillance system searches any unusual activity, it pushes the respective team, which is capable to stop otherwise help prevent few miss happening at that place. Today, the video surveillance is going to use in many areas like as: Facility protections, Operation monitoring, Parking, Traffic monitoring, shopping patterns and etc.

Marine Wildlife Preservation:

ML algorithms are also going to use to develop the behaviour models for endangered cetaceans and other marine species. It helps to scientists for regulating and monitors their populations.

Image Recognition:

Image recognition that makes deal with cataloguing and identify the feature otherwise an object in the digital picture. This is not important and focuses able machine learning and Artificial Intelligence technology. This concept works with for many further analyses like as face detection, face recognition and pattern recognition.

Predict Potential Heart Failure:

These ML algorithms are designed for scanning a doctor’s fee-form e-note and identify the patterns in the patient’s cardiovascular history for making waves in the medicine. Beyond of this, physician can also dig through multiple health records to reach at a sound diagnosis, and redundancy is now decreased along with the computers making and analysis based on the present information.

Social Media Services:

The social media companies are also using the machine learning for their own and user benefits, as well as customizing your news feed to get excellent ad targeting.

Also Read: Different Types of Machine Learning With Examples!

Facebook: Facebook adopts the ML and AI to detection of faces. When you are uploading the picture on FB, then it will get automatically reflects the faces and suggests friends tag. Moreover them:

  • It appears the ad of a specific business that is most relevant to uses’ interest.
  • It allows personalizing the news feed and making ensure to reflect posts that amuse one.

Snapchat: It serves the facial filter namely with ‘Lenses‘ that filter and keep track the all activity, permits the users to tag with animated pictures otherwise digital masks that can shift whenever their faces move.

Instagram: By using the machine learning algorithms, sentiments behind the emojis can be easily identified. IG also allows users make and auto-recommend emojis and its hash tags. There is the vast utilization of emoji across the all demographics that are going to use with describing and explore by IG at the huge scale via emoji-to-text translation.

Pinterest: It will get employ computer vision to automatically recognize object in the picture or ‘Pin’ and then recommend identically pins. It is also cover the spam prevention, discovery, email marketing, ad performance, and search along with using the ML and AI.

Online Fraud Detection:

GPay and PayPal are also good application of machine learning that help to track the transactions and differentiating in between the illegitimate and legitimate transactions. Hence, machine learning allows to get sound cyber security by preventing the online monetary fraud.

Traffic Alerts By Using Google Map:

Whenever, you are going to new place for vacations, then you can take assistance of Google Maps that shows you the perfectly path along with the shortest route and give the prediction of traffic conditions. If traffic conditions as per prediction are traffic is cleared, slow moving or heavily congested along with helping of two ways:

  • It shows the real Time location of the vehicle from sensors and Google Maps.
  • It takes the average time on past days at the same time duration.
  • Each one who is adopting the Google Map is assisting this app to make it better. It takes the information from the user and sends back to its database to enhance the performance

Most Intelligent Gaming:

Some people might remember the chess match that was done in between the Gary Kasparov and IBM’s Deep Blue, where the Deep Blue came out victorious. In 2016, Google DeepMind’s AlphaGod was defeated Lee Dedol and proceed to world champion. The ancient Chinees game of Go can be considered as much more hassle for the computers to learn and then to master as compare to chess. Thus, the artificial intelligent of AlphaGo was especially trained for playing Go and not by simply analysing the towards to move of the world’s best players but by devoting the practice against itself thousands of times.

Email Spam & Malware Filtering:

When, you receive the multiple emails, then it is filtered automatically as normal, spam, an important. It is also most popular example of the machine learning and AI. By helping the ML algorithms, you always receive the important email on your inbox along with the important symbol and spam emails in your spam box. There are few spam filters that are used by Gmail like as: Content Filter, Header filter, General blacklists filter, Rules-based filters, and Permission filters.

Few machine learning algorithms are going to use in the email spam filtering and malware detection along with Decision tree, Naïve Bayes classifier, and Multi-Layer Perceptron.

Creative Arts:

Today, latest songs are currently inspired by the algorithms for making music. For this process, it can be derived from enough data like as tons of conversations, speeches, newspaper titles, and helping to develop a lyrical theme. Different kinds of musical components can be created by computer such as Watson BEAT that can offer inspiration to songwriters. Artificial intelligent helps to musicians in understanding the preference of their fans and creating much more precise predictions about which song would be ultimately be hits. It is also one of the stunning applications of machine learning.

Online Customer Support:

Now these days, most of websites serve the option to chat with customer support representative, while they are getting to navigate within the websites. But, it is not possible to each website has a live executive to give replay your question. So, most of time, you talk to a Chabot. These bots helps to extract the information from your website and show it to the customers. Hence, the Chabot is getting more popularity with this time. They are able to understand the user queries better and offer them along with excellent answers. This is possible cause to its ML algorithms.

Real-Time Dynamic Pricing:

When you book an Uber in the peak office hours at the morning or evening, then you can get a difference in the prices as compared to normal hours. These rates can be hiked cause of surge applied by companies whenever getting to demand is higher. But, how it is possible to surge the price are determine and applied by companies. Hence, in which AI and machine learning technologies are working, which are:

  • Customers’ reaction on surge prices
  • It is adopted the suggesting optimum prices so that no harm of customer losing occurs to business.

Machine learning methods also assists for finding out the best price, discounted price promotional price and so on for every customer.

Speech Recognition:

Google allows user the best feature that is ‘Search by Voice’. It is a good example of speed recognition. In the speech recognition process, to convert the voice instruction into text format that is also called the ‘speech to Text’ otherwise ‘computer Speech Recognition’. Now these days, machine learning algorithms are mostly working in several areas of speech recognition like as Alexa, Cortana, Siri, and Google assistance; in which to use the speech recognition techniques to follow the sound instructions.

Search Engine Result Refining:

Most of all search engines (Google, Bing, Yendex, and etc.) adopt the machine learning technology to enhance the search results for their users. Each time you find any information with executing keywords, then algorithms at the backend keep a watch at how you gets respond the results. When, you open the top result and stay on the web page for longer time, the search engine assumes that this results is shown were at the depend on query. Same as; when you reach the second or 3rd page of the search results but do not open any results. Then, search engine gets estimation that the result offered did not perfectly match requirement. Hence, ML algorithms are working at the backend for improving the search results.


This is also one of the best applications of the machine learning is the extraction of the information. In this concept, the structured data is getting to extract from the unstructured data, and that is adopted in the predictive analytic tools. Hence, data is mostly found in a raw or unstructured form that is not helpful, and to make it helpful, the extraction process is used. For examples are:

  • For predicting vocal cord disorders to generating the model
  • Helping out the diagnosis and treatment of the problems quickly

Get Automation as Employee Access Control:

Large scaled firms are going to adopt the machine learning algorithms to identify the level of getting to access employees would need in several areas; it depends on their job profiles. It is also one of the coolest examples of machine learning.

Statistical Arbitrage:

The arbitrary is an automation trading process that is going to use in the finance sector to keep manage the enlarge amount of securities. This process adopts the trading algorithm to analyse a set of securities by using the economic variable and correlations. For examples are:

  • Analyse enlarge data sets
  • Analyses the market microstructure by helping the algorithmic trading
  • To detect the real-time arbitrary opportunities
  • To optimizes the arbitrage strategy to enhance outcomes

Product Recommendations:

Product recommendation is one of the most examples of the machine learning. Product recommendation works as the stark features of most of each e-commerce web portals today. By helping of machine learning, website lets you know about your behavior based on your last purchases, cart history, searching pattern and then make deal with the product recommendations.

Self-Driving Cars & Automated Transportation:

This is also one of the most amazing applications of machine learning is the self-driving cars. ML plays the major role in the self-driving cars. Tesla Company, the most eminent car manufacturing firm is working on the self-driving car. It is going to use unsupervised learning methods to train the car concepts to identify people and object while getting to drive a car.

Stock Market Trading:

Today, Machine learning is widely using in share marketing trading. In the share market, there is always getting a risk of up and down in the stock. Therefore, this machine learning’s long short term memory neural network is adopted for the prediction of share market trends.

Adopting for Dangerous Tasks:

As you know very well that bomb disposal is the most dangerous task on the planet. This is another application of machine learning, where the use of ML is most essential to keep save the lives. Todays, the robots and drones are also using for performing over the dangerous jobs. At that time; drones and robots have to need the human control but as machine learning evolvement.

Banking and Financial:

Today, banking and other financial firms are going to adopt the advanced technology that is machine learning. ML helps to their clients for getting to prevent fraud and protect account from the unwanted activities. The machine learning algorithms identify that what factors to consider making the filter to keep harm at bay. Most of websites, which are unauthentic will be automatically filtered out and most restricted from the initiating transactions.

Enhanced for ElderCare Experience:

Most of elderly people, their daily routing work can be a daunting one. Mostly trust on the help from outside for their elderly family members. Today, elderly care is getting a growing concern about families over the world. Its most remedy is ML enabled AI powered in the home robots. These robots are capable to help the elderly people with every day works, keeping them individually and in their home. Hence, it is improving their overall well-being. Today, AI researchers and medical have been piloted system at the based on infrared cameras that help to detect when elderly falls, restlessness, fevers, urinary frequency, chair and bed comfort, monitor food and alcohol consumption, fluid intake, sleeping, eating, and etc.

Cyber Security:

GPay and PayPal are also good examples of machine learning that help to track the transactions and differentiating in between the illegitimate and legitimate transactions. Hence, machine learning allows to get sound cyber security by preventing the online monetary fraud.

Agriculture Sector:

Machine learning is going to use in agriculture to offer the perfectly and effective farming along with minimal labour. As well as, ML provides the priceless crop related information and suggestions; thus framers are capable to decrease the losses.

Environment Protection:

Machine learning algorithms also help to boost up the environment sustainability. For the good example: is IBM’s Green Horizon Project, in which the environment statistics from assets and sensors are getting to leverage to generate the pollution forecasts. The main objective is to bring down the environment bad impacts.

Ads Recommendation:

Today, mostly people devote most of time on the Google or internet surfing. If, they want to send the web traffic on any specific page or website, then they run the many ads on every page. But, these advertisement are different on every user even whenever two users are going to use the identically internet and on the same region. These ads recommendations are applicable along with the help of machine learning algorithms. And, these ads recommendations are depend on the search history of every user. For instance, if anyone user tries to search for shoes on Amazon otherwise other e-commerce website, then he/she will get ads recommendation of shoes after sometime.

Automatic Language Translation:

This is also one of the most common machine learning examples is the language translation. Machine learning plays the major role into translation of language translation one to another. We are stunned that how the websites are capable to translate from one language to another and provide the contextual meaning as well. Behind of this, one translation tool is working that is known as the ‘Machine Translation’. It allows to people to make interaction along with others from all around the world. Without helping it, life would not be easily as it is now. It has offered the confidence to travellers and business man to safely venture into out of country along with conviction that language will no longer be a limiter.

Auto-Friend Tagging Suggestion:

It is also one of the eminent examples of the machine learning is the Auto-friend tagging suggestions service by offering Facebook. When we try to upload a new image on the FB along with friend, then it suggest to tag the friend and automatically offers the names. FB performs it by helping the DeepFace that is a facial recognition system made by Facebook. It is capable to get identify the images and faces as well.

Healthcare & Medical Diagnosis:

Machine learning makes deal with many techniques and tools along with diagnostic and prognostic problems in the diverse medical realms. The machine learning is widely going to use for many areas in the medical like as:

FAQs (Frequently Asked Questions)

What are the applications of machine learning in computer vision?

Machine learning is going to improve the computer vision about the recognition and tracking; and it serves the most intensively methods for the image processing, object focus, and acquisition that are used into computer vision. Hence, computer vision is getting to broaden scope of the machine learning.

What are the applications of machine learning in healthcare?

There are the top 10 real-world applications of machine learning in the healthcare and medical areas like as:

  • Drug Discovery & Manufacturing
  • Detection the Diseases & Diagnosis
  • Personalized Medicine
  • Medical Imaging Diagnosis (X-rays or MRI scans)
  • Smart Health Records
  • Machine Learning-based Behavioural Updating
  • Crowd sourced Data Collection
  • Clinical Trial and Research
  • Radiotherapy
  • Management of Medical Records

What industries use machine learning the most?

There are the 5 industries that are getting heavily trust on the Machine Learning and AI like:

  • Healthcare & Medical
  • Bank and Finance
  • Agriculture industry
  • Transportation industry
  • Retail and Customer Service

What are the applications of machine learning in drug discovery and development?

Machine learning and AI can be adopted intensively in many areas of the drug discovery such as drug design, chemical synthesis, polypharmacology, drug screening, and drug repurposing.

What are the applications of machine learning in mechanical engineering?

Machine learning is widely going to use into various areas like as making a new one concepts for the cars and aircraft along with design DNA. With helping of computer vision to identify the flaws during 3D printing, turning static drawings into active simulations with smart design tools.

What are the applications of machine learning in cancer prediction and prognosis?

Machine learning is widely going to use to derive risk cohorts, inform treatment plans, predict prognosis, and aid with diagnosis and early interventions. With the help of patient data’s proliferation, data-driven approaches can increase the understanding of cancer and their effect on individuals.

What are the applications of machine learning in finance?

There is some of the real-world applications of the machine learning in the finance domain like as:

  • Algorithmic Trading
  • High-Frequency Trading (HFT)
  • Fraud Detection
  • Loan/ Insurance Underwriting
  • Portfolio Management – Robo-Advisors
  • Document Analysis
  • Trade Settlements
  • Money-Laundering Prevention
  • Risk Management
  • Chatbots

What are the business applications of machine learning?

There are some application areas where you can use the machine learning effectively like as:

  • Predictive Maintenance
  • Financial Analysis
  • Image Recognition
  • Customer Lifetime Value Prediction
  • Increasing Customer Satisfaction
  • Eliminates Manual Data Entry
  • Detecting Email Spam
  • Medical Diagnosis
  • Improving Cyber Security

What are the top 10 uses of machine learning in real-life?

In this article, already we have been explored many applications where machine learning can be used effectively, you can check them.

What are the main uses of machine learning in daily-life?

Today, machine learning is mostly going to use into different areas like as email filters to sort out spam, websites to make personalized recommendations, internet search engines, banking software to detect unusual transactions, and lots of apps on our phones like as voice recognition.

Final Thoughts

From this article, you have been completely educated about various applications of machine learning as well as uses of machine learning across different industries with ease. If this article is useful for you, then please share it along with your friends, family members or relatives over social media platforms like as Facebook, Instagram, Linked In, Twitter, and more.

Also Read: 25 Advantages and Disadvantages of Machine Learning | Pros and Cons

If you have any experience, tips, tricks, or query regarding this issue? You can drop a comment!

Happy Learning!!

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