Examples of Machine Learning
In this page page, we will learn Examples of Machine Learning, Speech and Image Recognition, Traffic alerts using Google Map, Chatbot (Online Customer Support), Google Translation, Prediction, Extraction, Statistical Arbitrage, Auto-Friend Tagging Suggestion, Self-driving cars, Ads Recommendation, Video Surveillance, Email & spam filtering, Real-Time Dynamic Pricing, Gaming and Education, Virtual Assistants.
Machine Learning technology has drastically altered our lifestyles, as we have become increasingly reliant on it. It is a subset of Artificial Intelligence that we all use, whether consciously or unconsciously. For example, we use Google Assistant, which uses machine learning techniques, and we rely on online customer service, which is another form of machine learning.
Machine Learning employs statistical approaches to make a computer more intelligent, allowing it to automatically retrieve and utilize all business data. There are numerous examples of Machine Learning in the real world, including the following:
1. Speech and Image Recognition
Speech to text conversion is aided by computer speech recognition, also known as automatic speech recognition. Many programs convert live speech to an audio file format, which is then converted to a text file by another application.
Speech recognition applications include voice search, voice dialing, and appliance control. The most popular speech recognition software is Alexa and Google Home.
Image recognition, like speech recognition, is the most extensively used example of Machine Learning technology for identifying any object represented by a digital image. There are a few examples of image recognition in the actual world, such as,
As we've seen on Facebook, tagging the name on any photo. It's also used to split a single letter into smaller pictures in order to recognize handwriting.
Furthermore, facial recognition is the most well-known example of image recognition. We are all using latest generation smartphones that use facial recognition technology to unlock them. As a result, it contributes to the system's security.
2. Traffic alerts using Google Map
When someone sets out to find a specific location, Google Map is one of the most commonly utilized tools. The map aids us in determining the best or fastest route, as well as traffic and other pertinent information. But how does it provide us this information? Google Maps makes use of a variety of technologies, including machine learning, which collects data from a variety of users, analyzes it, updates it, and makes predictions. It can also notify us about traffic before we start our travel using predictions. While we are stuck in traffic, Machine Learning can help us choose the best and fastest route using Google Maps. We can also answer some inquiries, such as whether or not the route is still congested. This data and information is automatically recorded in a database, which Machine Learning utilizes to determine the exact location of other persons in traffic. Google maps may also be used to locate hotels, malls, restaurants, movie theaters, buses, and other venues.
3. Chatbot (Online Customer Support)
In every business, such as banking, medical, education, and health, a chatbot is the most extensively utilized software. Chatbots can be found in every banking application to provide consumers with speedy online service. These chatbots are likewise based on machine learning principles. Based on frequently requested questions, the programmers input some fundamental questions and answers. As a result, whenever a customer asks a question, the chatbot recognizes the question's keywords in a database and then provides the customer with an appropriate response. This aids in providing clients with rapid and efficient customer service.
4. Google Translation
Assume you're working on an international banking project that requires French, German, or other languages, but you only speak English. In that instance, you'll be in a panic because you won't be able to move further without reading paperwork. The Google Translator program aids in the translation of any language into the target language. As a result, you can translate French, German, and other languages into English, Hindi, or any other language in this manner. This simplifies the work of various sectors because a user can work on any country's project without difficulty.
Google's Neural Machine Translation detects any language and translates it into any other language.
Machine learning techniques are also used by the prediction system to make predictions. Predictions are employed in a variety of industries. In bank loan systems, for example, mistake probability can be calculated using machine learning predictions. The available data is sorted into different groups using a set of criteria provided by analysts, and the error probability is forecasted once the classification is complete.
The extraction of data is one of the best instances of machine learning. Structured data is extracted from unstructured data in this process, which is then employed in predictive analytics tools. The data is frequently found in an unstructured or raw form that is not valuable, and the extraction procedure is utilized to make it useful. The following are some real-world examples of extraction:
- Developing a predictive model for vocal cord diseases.
- Making the diagnosis and treatment of a problem go more quickly.
7. Statistical Arbitrage
Arbitrage is a type of automated trading used in the financial industry to manage a large number of securities. A trading algorithm is used to examine a group of securities using economic data and correlations. Here are some examples of statistical arbitrage:
- Algorithmic trading that examines the microstructure of a market
- Analyze large amounts of data
- Recognize opportunities for real-time arbitrage.
- Machine learning improves the arbitrage strategy by optimizing it.
8. Auto-Friend Tagging Suggestion
One of the popular examples of machine learning is the Auto-friend tagging suggestions feature by Facebook. When we upload a new photo to Facebook with friends, it prompts us to tag them and provides the names automatically. Facebook does it by using DeepFace, which is a facial recognition system created by Facebook. It identifies the faces and images also.
9. Self-driving cars
Self-driving cars are the future of the automobile industry. These are self-driving cars that use deep learning and machine learning concepts. Scale-invariant feature transform (SIFT), AdaBoost, TextonBoost, and YOLO are some of the most commonly used machine learning algorithms in self-driving cars (You only look once).
10. Ads Recommendation
Most people nowadays spend a significant amount of time on Google or exploring the internet. They also get many adverts on each page while working on any webpage or website. However, even when two users are using the same internet and are in the same location, these adverts are unique to each person. Machine learning algorithms are used to make these ad recommendations. These ad recommendations are based on each user's search history. For example, if a person looks for a shirt on Amazon or another e-commerce site, he will receive a series of advertisements recommending clothing after a period of time.
11. Video Surveillance
Video surveillance is a sophisticated use of artificial intelligence and machine learning that may detect any crime before it occurs. It is far more efficient than having several films monitored by a human because it is a much more difficult and boring work for a human to do so; this is why machines are the superior option. Video surveillance is highly important since it looks for certain human behavior such as standing stationary for an extended period of time, stumbling, or dozing on benches, among other things. When the monitoring system detects any strange activity, it notifies the appropriate team, which can then intervene to stop or prevent any mishaps from occurring.
The following are some examples of common video surveillance applications:
- Facility protections
- Operation monitoring
- Parking lots
- Traffic monitoring
- Shopping patterns
12. Email & spam filtering
When we receive a new email, it is automatically screened, which is an example of machine learning. Machine learning is the technology that allows us to receive essential messages in our inbox with the important symbol and spam emails in our spam box. Gmail employs the following spam filters:
- Content Filter
- Header filter
- General blacklists filter
- Rules-based filters
- Permission filters
Multi-Layer Perceptron, Decision Tree, and Nave Bayes classifier are some machine learning methods used in email spam filtering and malware detection.
13. Real-Time Dynamic Pricing
When we reserve an Uber during peak business hours in the morning or evening, the price is higher than during non-peak hours. Charges are raised as a result of enterprises charging surge prices when demand is high. However, how are these surge prices determined and implemented by businesses? AI and machine learning are the technologies at work here. These technologies address two major business issues:
- Customers' reactions to price hikes
- Suggestions for optimal prices so that the firm is not harmed by client loss.
- Machine Learning technology also aids in the discovery of discounted rates, best prices, promotional discounts, and other special offers for each consumer.
14. Gaming and Education
In gaming and education, machine learning technology is widely used. AI and machine learning are used in a variety of gaming and learning apps. Duolingo is a free language learning program that is created in a fun and interactive style among these apps. People feel as if they are playing a phone game while using this app.
It gathers information from the user's response and builds a statical model to determine how long a person can recall the word before needing a refresher, and it then provides that information.
15. Virtual Assistants
Virtual assistants, which are smart software incorporated in smartphones or laptops, are very prevalent in today's environment. These assistants serve as personal assistants and help with information requests over the phone. A virtual assistant interprets human or natural language voice commands and completes the work on behalf of the user. Siri, Alexa, Google, Cortana, and other virtual assistants are examples. To begin working with these virtual assistants, they must first be activated, after which we can ask them anything and they will respond. For instance, "What is the date today?" and "Tell me a joke," among others. AI, machine learning, natural language processing, and other technologies are employed in virtual assistants. Machine learning algorithms gather and evaluate data based on the user's previous participation and predict data depending on the user's preferences.