List of Top 10 Machine Learning Examples in Real Life

Introduction

Earlier humans were the only ones who had the potential to learn from their past experiences. But with technological progress, machines also evolved their competency to learn from experiences. This buzz about Artificial Intelligence and Machine Learning must have amused an average person. But knowingly or unknowingly, directly or indirectly, we are using Machine Learning in our real lives. It has become an inevitable part of our work and life. Now the question must be where, how, and when? The answer to all your question is this blog. Let’s dig in deeply about Machine Learning Examples to comprehend better.

History Of Machine Learning

The history of Machine Learning started in 1943. A Cornell University psychologist named Frank Rosenblatt was training and experimenting with building a machine that could recognize the letters in his name, alphabets Rosenblatt. (1957, 1959, 1960). This device adopted discrete and analog signals. Additionally, it had a threshold component that turned continuous impulses into discrete ones. It started as a model for contemporary artificial neural networks. Its learning theory was comparable to psychological human and animal learning theories. Rosenblatt himself carried out the first perceptron mathematical investigations. However, the Novikoff theorem Novikoff (1962), which outlines the prerequisites for a perceptron learning algorithm’s completion in a limited number of steps, has grown in popularity.

Why is Machine Learning so popular?

Why is Machine Learning so popular these days? It might be one of your first questions. It is because Machine Learning has the potential to process high-dimensional data quickly and accurately, also automate tasks and provide bespoke solutions irrespective of the field.

Below are several reasons – why Machine Learning is gaining popularity –

a) High data explosion:

A significant increase in data explosion has been caused by the internet and social media. A Machine Learning algorithm effectively analyzes the data and draws out insightful conclusions.

b) Better customer experience:

Machine Learning enables companies to tailor services and products to specific customer requirements. This results in improved customer experience and customer loyalty.

c) Automation:

Many manually performed activities can be automated more effectively and with lower error rates due to the Machine Learning algorithm.

d) Advances in computing

The emergence of cloud computing and high-performance computing has made machine model training more straightforward and affordable.

e) Accessibility

Tools and platforms for Machine Learning are getting easier to use.

1) Spam Filtering

It is simpler to handle our email because email providers use Machine Learning algorithms to filter out spam and unwanted messages from our inboxes.

2) Personalized recommendations

Online retailers and streaming services offer customized suggestions based on past behavior using Machine Learning algorithms.

3) Healthcare

Machine Learning made accurate healthcare diagnoses and therapies possible. As a result, healthcare expenses decreased, and patient outcomes improved.

4) Voice assistant

Siri and Alexa, among other virtual assistants, use Machine Learning. It responds to our voice and offers tailored help.

5) Image recognition

Machine Learning algorithms recognize an image’s individuals, objects, and other components.

Overall, Machine Learning has multifaceted applications in our daily lives. It significantly improves our quality of life.

1. Agriculture

Artificial Intelligence and Machine Learning are being used in many countries to improve the effectiveness and accuracy of their agriculture. In addition to increasing yields and excellent profits, it also makes higher-quality production possible. Additionally, Machine Learning reduces the necessity of labor. On the other hand, it offers insightful analysis and suggestions about crops to help farms reduce losses.

Uses of Machine Learning in Agriculture are:

a) Machine Learning helps to determine the field’s irrigation requirements.

b) It aids in detecting pest- or other field-related-caused harm to plants, trees, or other vegetation.

c) The condition of the soil health is also monitored using it.

d) It assists in locating irrigation system regions that are both under and over-watered.

Top examples of Machine Learning in Agriculture are:

1. Plantix: A mobile application called Plantix examines nutrient deficiencies and plant diseases.
2. California-based ConserWater: California-based ConserWater estimates the precise quantities of irrigation using satellite data, weather, and topography.

2. Healthcare and medical diagnosis

Humans prioritize healthcare. Many healthcare procedures are now carried out with extreme precision due to the advancement of Machine Learning. Managing big data sets, patient files and facilitating improved medical images are covered. Additionally, improving medical and diagnostics paved the way for thorough analysis and improved treatment diagnosis; ML proved that it outperforms the level of precision in this very segment. Additionally, it provides faster and more accurate findings, enabling prompt treatment.

There are several ways that Machine Learning is used in healthcare and medical diagnostics:

a) Machine Learning for enhanced medical imaging.

b) Smart Recording for more effective record-keeping and improved decision-making to provide patients better care.

c) Ensures patients’ reduced hospital stays.

d) Reduces the risk of hospital-acquired infections.

Top examples of Machine Learning in healthcare are:

1. Microsoft’s Project InnerEye: This assists medical professionals in Radiotherapy and surgical planning.
2. Tempus: It seeks to advance cancer research by gathering enormous quantities of clinical and medical data to support individualized treatments.
3. PathAI: With the aid of this technology, pathologists can make a faster and precise diagnosis.

3. Face detection

A biometric method called face recognition technology uses a person’s facial characteristics to identify them. A deep learning convolutional neural network is the Machine Learning algorithm used for facial identification. (CNN). It is a well-known artificial neural network for tasks involving image classification.

Uses of Machine Learning in Face detection are:

a) This technology is widely used for security reasons, such as preventing identity theft and identifying criminals.

b) Anthropometric readings using the Child Growth Monitor are no longer necessary.

c) This system has AL and can even learn to recognize a person’s appearance changes, such as when they put on weight or develop a beard.

d) Additionally, it can be used for more mundane jobs like locating a lost kid in a crowded area or spotting VIPs at an event.

Top examples of Machine Learning in Face detection are:

1. Luxand: It provides facial feature recognition solutions to biometric identification and security companies, banks, the entertainment industry, medical and cosmetic industries.
2. Blippar: The top Augmented Reality (AR) content provider Blippar uses Deep Learning computer vision to identify pertinent images and actual objects in the real world.
3. AppLock: It enables apps to be unlocked via voice and facial recognition so that users can access their financial, social media, and confidential information.
4. Digipass: To defend against malware assaults, Digipass employs two-factor authentication.

4. Finance

Machine Learning plays a crucial role in overcoming challenges related to financing. It has also cast light on a new, progressive, safer revolution in the banking industry. In place of conventional decisions based on client history, Machine Learning is quickly replacing them as the industry standard for security-related decisions. Machine Learning also enhances decision-making by evaluating the viability of loans or waivers using customer records and other variables.

Uses of Machine Learning in the Finance sector are:

a) Better process automation is made possible by Machine Learning.

b) It protects against fraud and provides better customer support with explicit options.

c) It offers recommendations based on user history to evaluate possible requirements.

d) Machine Learning applications are being developed for credit card fraud detection.

Top examples of Machine Learning in Finance are:

1. Paypal: Money trafficking is recognized and prevented by Paypal using Machine Learning.
2. Citibank and Freedzi: Citibank and Freedzi collaborate to detect and prevent online and in-person banking fraud.
3. Robotic financial advisers: Robotic financial advisers offer automated advice and support.
4. AI-driven hedge: Machine Learning is used by AI-driven hedge funds to execute stock transactions automatically.

5. Government Industry

Government organizations can increase efficiency and accuracy through innovations like Machine Learning to build themselves with the broader public. Government officials can track and manage large quantities of data related to finance, the economy, security, or other factors. It also helps in various scenarios, like identifying the possibilities of war and calculating decisions in unforeseen situations.

Uses of Machine Learning in the Government Industry are:

a) It helps to track criminals and missing children.

b) It is possible to anticipate where potholes will appear based on how well roadways are constructed.

c) It aids in predicting political deviations based on the collected data. The data gathered helps in forecasting political deviations.

d) Law enforcement organizations use data analysis in real-time to look for threats and anomalies, which aids in finding criminals and missing children.

6. Cyber security

Cyber assaults are becoming more frequent and are increasing exponentially. Machine Learning has evolved to address new cyber threats. It can examine vast amounts of data, look for patterns, and identify attacks early, revealing network weaknesses and predicting the timing and vector of upcoming cyberattacks. Additionally, it increases internet security by reducing online financial fraud.

Uses of Machine Learning in Cyber Security:

a) Machine Learning systems recognize and handle cyber attacks.

b) Cybersecurity software, Machine Learning, and natural language processing can protect employees from cyberattacks.

c) Cybersecurity problems can be identified, and assaults can be thwarted using Machine Learning.

d) By leveraging Machine Learning techniques, user behavior can be recognized, and cybersecurity teams can be notified of any problems.

Top examples of Machine Learning in Cyber Security:

1. Cylance: Antivirus software from Cylance learns to identify viruses without using signatures.
2. Versive: It provides cybersecurity software in conjunction with AI.
3. Darktrace: It uses Machine Learning to find systemic behavioral trends.
4. Crowdstrike: It uses Falcon Platform to defend against online threats.

7. Traffic alerts using Google Map

Millions of users can get helpful instructions and up-to-date traffic data from Google Maps. Google Maps can automatically extract data from geo-located imagery to increase accuracy, precision, and regularity. Scalable location data delivery depends on robust training data and extensive image gathering.

Uses of Machine Learning in Traffic alerts using Google Maps:

a) Google Maps utilizes cutting-edge Machine Learning methods and historical knowledge to predict traffic.

b) It increases the precision of the location data users of the Google Maps Platform can access.

c) For its end users, it provides dependable geospatial encounters.

d) Google Maps uses predictive traffic models to automatically locate less congested routes when there is heavy traffic.

8. Chatbot (Online Customer Support)

Chatbots are Artificial Intelligence (AI) apps that develop automated responses to user inputs using Machine Learning and deep learning algorithms. They are utilized in marketing, client service, and instant messaging. Almost every business uses chatbots, including the banking, medical, educational, and health sectors.

Uses of Machine Learning in Chatbots:

a) Chatbots can interpret the context of a conversation using Machine Learning and then react appropriately.

b) Chatbots utilize the Machine Learning concept and provide customers with efficient online assistance.

c) Machine Learning enables Chatbots to understand and react to inputs in natural language.

d) Personalizing the robot user interface through Machine Learning is possible.

Top examples of Machine Learning in Chatbots are:

1. Netomi: 70% of client questions are answered automatically by the AI platform of Netomi, which has the best accuracy of any chatbot for customer service.
2. AtSpoke: It is an internal tagging framework that enables internal groups to naturally recognize 40% of requests and respect goals 5x more quickly.
3. WP-Chatbot: The most widely used chatbot in the WordPress community, WP-Chatbot offers live and automated interactions via a local Messenger talk gadget.
4. Microsoft Bot Framework: The Microsoft Bot Framework is an open-source, visual authoring material for creating casual encounters with language understanding, QnA making, and bot answers.

9. Self-driving cars

The role of Machine Learning in autonomous vehicles enables the vehicle to learn from data and make predictions about the world. Machine Learning algorithms can predict the behavior of objects, pedestrians, people, and other vehicles on the road. The algorithms enable the vehicle to gather environmental information from cameras and other sensors. Machine Learning is used by cars to learn and carry out these tasks as well as (or better than) humans.

Uses of Machine Learning in Self-driving cars:

a) It monitors the driver’s eye motions to determine his health condition.

b) Since 94% of fatal crashes are caused by human error, they help to improve safety.

c) The goal is to make the car as precise and safe as feasible while minimizing the human factor in driving.

d) Reduced expenses are an additional benefit of autonomous vehicles.

Top examples of Machine Learning in Self-driving cars:

1. Tesla: It is working on an autonomous driving system called Autopilot, which has some of the most cutting-edge AV systems on public roadways but is still far from fully self-driving.
2. Rivian: It is an American auto and energy business specializing in electric and autonomous driving.
3. Wayve AI: A technology start-up in London named Wayve AI creates artificial intelligence programs for self-driving cars.

10. Ads Recommendation

Nowadays, most people surf the internet or spend hours on Google. They also encounter numerous ads on each page while browsing any website or webpage. However, even when two users use the same internet and the exact location, these ads are different for each person. Machine Learning algorithms are used to make these suggestions for advertisements. These ad recommendations are dependent on each user’s search history. For instance, if a user looks for a shirt on Myntra or another e-commerce site, other ads recommending shirts will appear after a while.

Uses of Machine Learning in Ads Recommendation:

a) Machine Learning predicts which ads are most relevant and effective for users.

b) Machine Learning can segment users into different groups, allowing advertisers to tailor their ads and improve their relevance.

c) Machine Learning algorithms can personalize ads to increase engagement and conversion rates.

d) Advertisers can use Machine Learning algorithms to optimize bidding strategies to maximize ROI and reach their target audience more effectively.

Top examples of Machine Learning in Ads Recommendation:

1. Netflix: It uses Machine Learning in its recommender system to decide which content to add to its streaming platform and optimize the production of original TV series and movies.
2. Spotify: On Spotify, the collaborative filtering algorithm compares user-created playlists to other songs and recommends them.
3. Amazon: Amazon’s personalized recommendation engine improves customer experience and presents products based on browsing history.

1. CCTV & Video Analyzing Tool to Understand Customer Behavioral Patterns

Analyzing and studying the actions with recorded videos on CCTV becomes difficult. Therefore we came up with a solution. EagleEyeViewer uses Machine Learning technology to collect data on customers’ movements and gestures to determine where they spend more time and what interests them more.

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2. An AI-Based Platform to Suggest Accurate Therapy

It is an online doctor-patient consulting app that allows youngsters to share their thoughts and symptoms and get timely help through consultation. AI algorithms suggest accurate therapy and user health understanding based on thoughts, calls, and chats.

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3. Intelligent Tool for Prosthesis Measurement

It is a mobile app for fixed prosthesis. The only tool dental assistants need is to help the dentist, labs, and the patient. The core code is attached to a patient, lab, and crown, which serves as a unique identifier. Dentists fill out a digital form to take measurements, which is prefilled based on specific combinations of answers.

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