Machine Learning Algorithms - Top 5 Real Life Examples (2023)

Machine learning has brought about significant changes to the business processes of many companies. And we're not just talking about futuristic products like Siri and Amazon Echo or companies that allocate considerable budgets to R&D programs like Google and Microsoft. In fact, almost every Fortune 500 company is making more money and working more efficiently thanks to machine learning.

What is machine learning (ML)?

Machine Learning (ML) is a data analysis technique that enables an analytical system to learn while solving many similar problems. Machine learning is based on the idea that analytics systems can learn to spot patterns and make decisions with minimal human input.

Let's imagine there is a program that can analyze last week's weather, as well as readings from a thermometer, barometer, and anemometer to make a forecast. Ten years ago, they would have written an algorithm with lots of conditional (if) constructs to do this:

  • If a strong wind is blowing, there are likely to be clouds.
  • If there are clouds, it will be cloudy.
  • If the temperature has dropped but is above freezing, it will rain; if it's below freezing, it's going to snow.

The programmer had to describe an incredible number of conditions for the code to be able to predict the change in weather. At best, a multivariate analysis of the data was used, but all regularities were manually specified, even in this case. And even if that program was called artificial intelligence, it was just an imitation.

On the other hand, machine learning allows the program to build cause and effect relationships independently. The AI ​​is given a task and learns to solve it. The computer can analyze indicators over several months or even years to determine what factors influenced climate change.

Here's another great example from Google's DeepMind:

Google's DeepMind taught a model to walk. The program received information from virtual receivers and its objective was to move the animated avatar from point A to point B. There were no instructions for this. The developers have just created an algorithm that the program learned. As a result, he was able to complete the task on his own.

The AI, like a child, tried different methods to find the one that best achieved the result. The characteristics of the physical models of the avatar were also taken into account, making the quadruped jump and the humanoid run. The AI ​​was also able to balance on moving slabs, navigate around obstacles, and navigate off-road.

What is machine learning for?

In the example above, the walk was described. This will help humanity create trainable robots that can adapt to the tasks at hand. For example, putting out fires, clearing debris, mining, etc. Machine learning is much more effective than a regular program in these cases. A human being can make a mistake while writing the code. And that can cause the robot to go into a stupor because it doesn't know how to interact with a rock in a way that the developer didn't prescribe.

Machine learning is widely used in data science. And a significant part of the time, those tasks are marketing.

(Video) Top 10 Machine Learning Algorithms | THIS RULES THE WORLD

Amazon uses AI with machine learning to offer users the product they are most likely to buy. To do this, the program analyzes the experiences of other users to apply them to new users. Still, the system has its drawbacks; For example, when a user buys a hat, he sees offers to buy more. The show will conclude that a few hundred more won't hurt, as a hat was needed.

Google uses a similar system to select relevant ads and has the same problem. It is enough to search for information about what kind of bicycles there are, and Google will decide that the user wants to delve into this topic.

You can also work with voice assistants like Siri. They use ML-based speech recognition systems. In the future, they will be able to replace secretaries and call center operators.

ML applications can be many different things. It can even help createquantum resistant blockchainIn the future. And you can use it in your applications. But you will need to buy, configure, and maintain a machine learning infrastructure.

Machine learning in the factory

Minimize production downtime.Due to breakdowns, malfunctions, or raw material shortages, downtime can cost a factory millions of dollars. Machine learning can help prevent them. This is done by collecting data from sensors on the computer and seeing which metrics fail. In the future, this information can be used to predict when and why downtime will occur and how to avoid it.

For example, it may be that the temperature in a store always rises before a machine breaks down. So if the temperature rises, the system will alert the engineers and they will prevent the problem in time.

To prevent downtime in mining operations, the oil and gas equipment manufacturerGE Oil and Gasuses the Industrial Internet of Things and machine learning. The company's platform collects data on the state of oil production and schedules diagnostic checks and helps identify failures before they happen. The same rig helped the Kuwait Oil Company increase gas production by 2-5%, and Malaysia's Petronas cut maintenance costs by 10%.

Creation of a production management system.With the help of sensors and machine learning, it is possible not only to perform restricted tasks (such as avoiding breakdowns), but also to manage the entire production:

  • Reduce the percentage of rejected parts: Analyze why rejects occur and how to avoid them.
  • Streamline individual steps so they take less time.
  • Use less material for production and therefore reduce costs.
  • Monitor the health of the equipment and record its efficiency and workload.
  • Automate individual production steps.

Microcontroller maker Simatic uses a platform based on IoT and machine learning. It helps to collect and analyze information from sensors on equipment in real time. It helped automate the production of thousands of products by 75%, increased production by 9 times with the same physical space and personnel, and reduced waste by almost 100%.

Identification of security threats.Machine learning helps make production safer by identifying small changes in equipment performance and giving early warning of potential disasters.

For example,shell energy companyuses machine learning, neural networks, and IoT to identify security threats and automatically alert employees. That way, they can react to a problem before disaster strikes. By the way, Shell also uses machine learning to optimize production and mining operations.

Exploration of new fields.One of the main problems in the oil, gas and mining industry is the difficulty in discovering new deposits. Machine learning helps speed up this process. Based on data from previous fields, artificial intelligence builds models that predict where to look for new gas or mineral deposits with high precision.

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machine learning in finance

Credibility assessment.Usually, in banks, managers assess the creditworthiness of a client. Employees spend a lot of time evaluating and often make mistakes. They refuse loans to those who can pay and give loans to the insolvent.

The algorithm to assess the credibility of bank customers can be taught. To do this, it is loaded with information about previously issued loans: if they were repaid or not, if there were delays or early repayments. All this helps the bank to automate the issuance of loans.

Citibank has developed a robust system that assesses creditworthiness. In particular, the system is polished to detect fraudulent credit card transactions when shopping online.

Fight against fraud.Banks and their customers often lose money in fraudulent transactions. Machine learning helps identify these transactions. Unique algorithms learn to detect signs of fraudulent transactions and block them in time.

Machine learning can be used to detect fraudulent account attempts, identify the identity of a customer without a passport, recognize ATM camera scammers, and analyze customer credibility. Many banks have examples of machine learning to prevent fraud. For example,danish bankreduced the percentage of false fraud claims by 60%.


If your business is generating incremental sales or is in a high growth mode, this article will be perfect for you as it will guide you properly toML in demand forecasting.

Examples of machine learning in medicine

Improving customer service. The faster the appointment process in the clinic, the shorter the queues, the more comfortable it is for doctors to work and the more loyal the patients. For example,studentuses ML to provide students with the most relevant documents.

The Invitro network of clinics has implemented a facial recognition system. As soon as a patient walks up to the counter, the administrator sees the correct card on the computer and sends it to the correct office. This helps avoid queues at peak times, simplifies the work of receptionists and serves more patients.

(Video) Top 10 Machine Learning Algorithms for beginners!

Disease diagnostics.If you upload examination and diagnostic data into the program, you can be taught to diagnose in the same way as doctors.

For example,Corti's artificial intelligencelistens for ambulance calls and recognizes cardiac arrest based on callers' responses, voices and breathing. In an experiment, the program recognized 93.1% of cardiac arrests. Humans normally recognize 72.9%. Also, Corti works faster. She diagnoses in 48 seconds, compared to 79 seconds for humans.

Now the system is being implemented in several European cities. You will work in the emergency services together with the dispatchers. In the video, the AI ​​can be heard talking to the person who called the ambulance.

Automated robotic surgery.Machine learning helps teach medical robots to operate on patients on their own, taking many factors into account.

Machine Learning Algorithms - Top 5 Real Life Examples (4)

At the University of California, thethe robot watched 78 surgical moviesto teach her to sew. Thanks to this training, the robot was able to sew fake wounds, but with an accuracy of around 85%. For actual work, this is still not enough. Perhaps in the future these robots can be used to automate some operations. The video shows the learning process of the robot.

Machine Learning Algorithms - Top 5 Real Life Examples (5)

Application of Machine Learning in Retail and Marketing

Predict shopper actions, personalized offers, and advertising. A trained algorithm can predict customer behavior in the following ways:

  • Determine who will make a purchase soon.
  • Understand who prefers what products and recommend them.
  • Offer personalized discounts to encourage purchases.

For example, the Rive Gauche cosmetics chain uses machine learning to send personalized offers to customers. The program determines which customers are likely to buy in the next two weeks, which products are best offered to them, and at which discounts. Customers who worked with the system had a 42% higher average check and repeat purchases were 47% higher.

Demand forecasting and purchasing automation.Machine learning helps analyze customer actions and inventory balances to understand what, when, and in what quantities to buy.

British supermarket chainMorrisons uses machine learningto predict what items will be purchased and when. The system takes many factors into account, such as holidays and weather. As a result, the network was able to reduce supply gaps by 30%.

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Machine learning in logistics

Fuel savings and increased transport productivity. Fuel is one of the main cost elements in logistics. With the help of machine learning, it is possible to reduce your consumption: optimize routes or understand how to reduce the number of vehicles, preserving productivity.

Caterpillar Marine Divisionimplemented machine learning to save resources. The company installed sensors on the ships' equipment. More generators with smaller capacity have been found to run more efficiently than maximizing the use of multiple generators. This solution saved over $650,000 in one year.

Avoid supply interruptions. Delaying even one vehicle disrupts the entire supply chain: downtime, lost money, and customer dissatisfaction. Machine learning helps you avoid this by anticipating risks, preventing them early, and adjusting delivery times to account for all factors.

DHL uses artificial intelligence from Supply Watch. It monitors a variety of risks—weather, environmental factors, traffic congestion, and even crime rates—to proactively inform customers of potential delivery delays.

machine learning problems

Working with self-learning algorithms is complicated. Common programs are predictable. We can analyze them and understand how they work. With self-learning algorithms, the situation is more complex due to some problems:

  • Businesses often run into complex machine learning problems that are difficult to break down into simple sub-problems.They jump from one experiment to the next as they try to solve these complex use cases. This makes it difficult to speed up AI development.
  • Effective training of neural networks and complex algorithms requires a huge amount of data and technical resources: servers, special rooms for them, impeccable high-speed Internet, and plenty of electricity. It takes years and millions of dollars to get the data you need. Only a large IT corporation can afford such costs. There are not many open data sets, some can be bought but they are very expensive.
  • As the capacity to collect and process data sets increases, the harmful emissions produced by major data centers also increase.
  • Not only does the data need to be collected, but it also needs to be tagged so the machine knows exactly where the object is and what its attributes are. This applies to numeric data, text, and images. Again, you need millions of dollars in investment to do this manually.
  • Even if there is a lot of data and it is updated regularly, it may happen that the algorithm does not work in the training process. The problem can be both in the data and in the approach itself: when a machine successfully solves a problem with some data but fails to scale the solution with new conditions.
  • Despite all the advances in deep learning of neural networks, AI is still not capable of creating something completely new, going beyond the proposed conditions and exceeding its inherent capabilities. In other words, it is still not capable of surpassing humans. But they can perform tasks better than unskilled people. For example, a robot will stitch up a human wound better than an unskilled person. But a qualified surgeon will still do a better job.

Despite these issues, we will soon be able to significantly expand our capabilities with AI, transferring routine and expensive operations to it, communicating and controlling machines using neural interfaces.


The principle of machine learning makes it possible to create computers and programs that mimic human thinking. But unlike humans, they don't get tired, can make fewer mistakes, work with any amount of data, and evaluate it impartially. This opens up a vast field of possibilities for artificial intelligence.

Machine learning participates in the accelerated development of technologies. With it, we will be able to solve even tasks beyond the control of the human mind. And the examples of current ML applications are proof of this.

(Video) Top 10 Applications of Machine Learning | Machine Learning Applications & Examples | Simplilearn

With machine learning, you can create many services and programs. From the simple ones we use every day – browsers, facial recognition cameras, recommendation services – to the complex tools we need in industry or security. Our IT team has in-depth knowledge of. Please feel free toContact Us, the Jelvix team will be happy to help you!


Machine Learning Algorithms - Top 5 Real Life Examples? ›

There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

What are real life examples of machine learning? ›

Real-World Examples of Machine Learning (ML)
  • Facial recognition. ...
  • Product recommendations. ...
  • Email automation and spam filtering. ...
  • Financial accuracy. ...
  • Social media optimization. ...
  • Healthcare advancement. ...
  • Mobile voice to text and predictive text. ...
  • Predictive analytics.

What are real life examples or were we using ai ml and dl? ›

10 Real World Examples of Deep Learning Models & AI
  • Computer vision. ...
  • Sentiment based news aggregation. ...
  • Bots based on deep learning. ...
  • Automated translations. ...
  • Customer experience. ...
  • Autonomous vehicles. ...
  • Coloring illustrations. ...
  • Image analysis and caption generation.
Feb 22, 2021

What are the 4 machine learning algorithm? ›

There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.

Is Netflix an example of machine learning? ›

The reason why Netflix's services are so popular worldwide is that the company uses cutting-edge technology like artificial intelligence and machine learning to provide consumers with more appropriate and intuitive suggestions.

Is Google an example of machine learning? ›

Google services, for example, the image search and translation tools use sophisticated machine learning. This allows the computer to see, listen and speak in much the same way as humans do.

What are 2 main types of machine learning algorithm? ›

There some variations of how to define the types of Machine Learning Algorithms but commonly they can be divided into categories according to their purpose and the main categories are the following: Supervised learning. Unsupervised Learning.

What is machine learning with simple example? ›

Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own.

What are the main 3 types of ML models? ›

Amazon ML supports three types of ML models: binary classification, multiclass classification, and regression.

What are three real life example of AI? ›

Apple's Siri, Google Now, Amazon's Alexa, and Microsoft's Cortana are one of the main examples of AI in everyday life. These digital assistants help users perform various tasks, from checking their schedules and searching for something on the web, to sending commands to another app.

What are the two real life example of AI? ›

Built-in smart assistants on our phones like Siri, Alexa, and Google Assistant are the more obvious examples of AI that most of us are aware of and use. More mobile technology platforms are developing solutions that use AI for managing different aspects of the device like battery management, event suggestions, etc.

What is the easiest machine learning algorithm? ›

K-means clustering is one of the simplest and a very popular unsupervised machine learning algorithms.

Is CNN a machine learning algorithm? ›

A convolutional neural network (CNN or convnet) is a subset of machine learning. It is one of the various types of artificial neural networks which are used for different applications and data types.

What are three 3 main categories of AI algorithms? ›

There are three major categories of AI algorithms: supervised learning, unsupervised learning, and reinforcement learning. The key differences between these algorithms are in how they're trained, and how they function.

What is the best example of machine learning? ›

Image recognition. Image recognition is a well-known and widespread example of machine learning in the real world. It can identify an object as a digital image, based on the intensity of the pixels in black and white images or colour images.

Is Amazon a machine learning? ›

Amazon Machine Learning is an Amazon Web Services product that allows a developer to discover patterns in end-user data through algorithms, construct mathematical models based on these patterns and then create and implement predictive applications.

Is Tesla an example of machine learning? ›

The current AI technologies that Tesla is using in its cars are based on unsupervised machine learning.

Is Facebook an example of machine learning? ›

Machine learning is a system that learns as it receives new data, without being explicitly programmed, to carry out complex tasks quickly and efficiently. Facebook uses machine learning to generate the estimated action rate and the ad quality score used in the total value equation.

What is AI and machine learning examples? ›

The main applications of AI are Siri, customer support using catboats, Expert System, Online game playing, intelligent humanoid robot, etc. The main applications of machine learning are Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc.

What are most popular ML types in digital fluency? ›

There are two main types of machine learning: supervised learning, suitable for specific tasks we already know of, and unsupervised learning, which is good for discovering things we didn't already know.

Which algorithm is best for prediction in machine learning? ›

1. Linear Regression. Linear regression is perhaps one of the most well-known and well-understood algorithms in statistics and machine learning. Predictive modeling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explainability.

What is the step 5 in machine learning? ›

5. Train Model. Now the next step is to train the model, in this step we train our model to improve its performance for better outcome of the problem. We use datasets to train the model using various machine learning algorithms.

Where is machine learning used today? ›

Machine learning is used in internet search engines, email filters to sort out spam, websites to make personalised recommendations, banking software to detect unusual transactions, and lots of apps on our phones such as voice recognition.

How do you explain machine learning to a child? ›

You can explain machine learning to older kids in simple words by saying how it simulates human learning patterns to learn, grow, update, and develop itself by continually assessing data and identifying patterns based on past outcomes.

Is Siri a machine learning? ›

Over the years, Apple has added several features and capabilities to Siri, making it one of the most useful and advanced assistants. Now, it uses deep learning technology and machine learning algorithms to power its processing abilities.

What are the 3 C's of machine learning? ›

One popular framework for understanding the key elements of machine learning is the "3 C's" model, which stands for "Correctness, Consistency, and Completeness."

What are the biggest ML models? ›

List of large language models
NameRelease dateDeveloper
LaMDA (Language Models for Dialog Applications)January 2022Google
GPT-NeoXFebruary 2022EleutherAI
ChinchillaMarch 2022DeepMind
PaLM (Pathways Language Model)April 2022Google
25 more rows

What is the difference between AI and ML? ›

An “intelligent” computer uses AI to think like a human and perform tasks on its own. Machine learning is how a computer system develops its intelligence. One way to train a computer to mimic human reasoning is to use a neural network, which is a series of algorithms that are modeled after the human brain.

What is a famous AI in real life? ›

Voice assistants are the best AI examples in real life such as, Google Assistant, Alexa, or Siri. They take your question via voice, and process it using the Speech Recognition and Natural Language Processing systems on your phone and output the results via speech or text.

What are the five examples of artificial intelligence? ›

Artificial Intelligence Examples
  • Manufacturing robots.
  • Self-driving cars.
  • Smart assistants.
  • Healthcare management.
  • Automated financial investing.
  • Virtual travel booking agent.
  • Social media monitoring.
  • Marketing chatbots.

What is a strong example of AI? ›

Here are some examples: Self-driving cars: Google and Elon Musk have shown us that self-driving cars are possible. However, self-driving cars require more training data and testing due to the various activities that it needs to account for, such as giving right of way or identifying debris on the road.

What is an example of AI that you encounter every day? ›

Voice assistants, image recognition for face unlock in cellphones, and ML-based financial fraud detection are examples of AI software currently being used in everyday life.

What are some examples of AI in the home? ›

Some of the simplest ways to use artificial intelligence in homes include:
  • Smart cameras.
  • Smart assistants.
  • Kitchen appliances.
  • Smart thermostats.
  • Smart plugs.
  • AI cleaners.
  • Door locks.

What is the most advanced AI today? ›

Google DeepMind — AlphaGo

AlphaGo is considered to be one of the most intelligent AI systems in the industry due to its advanced capabilities and its ability to learn and adapt over time.

How is AI used in healthcare? ›

Deep learning AI can be used to help detect diseases faster, provide personalized treatment plans and even automate certain processes such as drug discovery or diagnostics. It also holds promise for improving patient outcomes, increasing safety and reducing costs associated with healthcare delivery.

Is Siri an artificial intelligence? ›

Siri is Apple's virtual assistant for iOS, macOS, tvOS and watchOS devices that uses voice recognition and is powered by artificial intelligence (AI).

What are the real world use cases of ML? ›

We may or may not be aware that machine learning is used in various applications like – voice search technology, image recognition, automated translation, self-driven cars, etc.

Which is best first algorithm in AI? ›

The best First Search algorithm in artificial intelligence is used for for finding the shortest path from a given starting node to a goal node in a graph. The algorithm works by expanding the nodes of the graph in order of increasing the distance from the starting node until the goal node is reached.

Which ML algorithm is best for small data? ›

Btw: If the dataset is extremely small, you may want to use SVMs, Decisiontrees or especially bayesian Networks. For small datasets, one thing one must avoid is 'overfitting the data' hence simple machine learning like 'Logistics Regression, Linear Regression and Bayesian Linear Regression will do fine...

Is Yolo a type of CNN? ›

YOLO algorithm employs convolutional neural networks (CNN) to detect objects in real-time. As the name suggests, the algorithm requires only a single forward propagation through a neural network to detect objects.

What does the ReLU stand for? ›

ReLU stands for Rectified Linear Unit. The main advantage of using the ReLU function over other activation functions is that it does not activate all the neurons at the same time. This means that the neurons will only be deactivated if the output of the linear transformation is less than 0.

What is TensorFlow and CNN? ›

CNNs are used for a variety of tasks in computer vision, primarily image classification and object detection. The open source TensorFlow framework allows you to create highly flexible CNN architectures for computer vision tasks.

What are the top 3 technology direction of AI? ›

In this article, I will explain three major directions of artificial intelligence technology, that are speech recognition, computer vision, and natural language processing.

What is the difference between an algorithm and an AI? ›

An algorithm is a set of instructions that tells a computer what to do. It can be as simple as adding two numbers together or as complex as solving a difficult mathematical problem. An AI is a computer system that can learn and make decisions independently.

How is machine learning used everyday? ›

Machine learning is already a part of your daily life

Alexa, Siri, Cortana, and Google Assistant all use machine learning to recognize what you say and decide how to answer you. Amazon and Netflix use machine learning to decide what movies, TV shows, and books to recommend to you.

What is machine learning in today's world? ›

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

What is the most common use of machine learning? ›

What are the Common Applications of Machine Learning?
  • Image Recognition. ...
  • Speech Recognition. ...
  • Predict Traffic Patterns. ...
  • E-commerce Product Recommendations. ...
  • Self-Driving Cars. ...
  • Catching Email Spam. ...
  • Catching Malware. ...
  • Virtual Personal Assistant.
Dec 19, 2022

What are the 3 uses of machine learning? ›

Few of the major applications of Machine Learning here are: Speech Recognition. Speech to Text Conversion. Natural Language Processing.

What is machine learning with example? ›

Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own.

What is machine learning in real time? ›

Real-time machine learning is when an app uses a machine learning model to autonomously and continuously make decisions that impact the business in real time.

How is machine learning used in society? ›

Machine learning is changing the world by transforming all segments including healthcare services, education, transport, food, entertainment, and different assembly line and many more. It will impact lives in almost every aspect, including housing, cars, shopping, food ordering, etc.

Why machine learning is important in daily life? ›

From tailored streaming platform suggestions to fraud detection in financial transactions, machine learning is improving daily life. In ML, Artificial Intelligence is used to create algorithms that can learn from data and make predictions or decisions based on that learning.

What is the role of machine learning in human life? ›

Machine learning is important because it gives enterprises a view of trends in customer behavior and operational business patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google, and Uber, make machine learning a central part of their operations.

Where is machine learning used in industry? ›

In manufacturing, machine learning can be used for quality control, automation and customization. For example, machine learning can be used to detect defects in products before they reach consumers. It can also be used to automate repetitive tasks such as assembly line work.

What are the 3 most common types of machine learning? ›

The three machine learning types are supervised, unsupervised, and reinforcement learning.

What are the examples of machine learning in industry? ›

Quality systems: machine learning algorithms create models that allow, for example, to detect defects in parts. Surface type defects in manufacturing, painting, etc. They also allow quality checks in an assembly process, presence or absence of parts, inspect welds, etc.


1. Computer Scientist Explains Machine Learning in 5 Levels of Difficulty | WIRED
2. Types Of Machine Learning | Machine Learning Algorithms | Machine Learning Tutorial | Simplilearn
3. Machine Learning Algorithm- Which one to choose for your Problem?
(Krish Naik)
4. Types of Machine Learning | Types of Deep Learning Algorithms with Real Life Examples
5. 10 Best Examples of Artificial Intelligence (AI) | Power of Artificial Intelligence in Real Life
6. Machine Learning Foundations: Ep #5 - Classifying real-world images
(Google for Developers)


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