Here are some examples of daily activities you may be doing, and it depends on aidata science and ml.
Search Engine
Google, Bing, and other search engines use sophisticated use ml How to find and rank web pages that match your search criteria. These engines are not just used ml To provide relevant results for you, they also have data science and other services. ml So, every time you search for something, the backend algorithm monitors the response.
This allows these engines to adjust search results.
Virtual Personal Assistant
Have you used Alexa, Siri, or Google Home? All of these virtual personal assistants apply data science to apply complete tasks such as answering simple questions, communicating news and weather, playing music and podcasts. To do this, they gather information about what you are saying and when, where and how you are saying things. These assistants use this information to produce results that suit your taste. They use it too ml In:
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Understanding You (voice processing and understanding)
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Improve performance based on previous interactions
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Go back to you and communicate (dialogue management)
Traffic Status
Have you ever wondered how the Traffic or Maps app can tell you which section of your commute is heavier? That's because they're using the user's GPS location and speed and adding it to a central server that manages traffic. Data Science Methods are used to construct a map of current traffic and estimate the density of traffic. In areas where GPS information may not be available, ml Historical data can be used to predict areas with high traffic.
Loan approval
Banks and other financial institutions gather extensive information about customers applying for loans. Data Science is used to find relevant data ml It is used to classify customers as to whether they are eligible for loans, based on the history and history of people with similar profiles.
Activity Tracker
Physical activity trackers such as Fitbit collect a huge amount of information about users. The data collected includes:
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Covered steps
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The floor has climbed
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The calories burned
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Sleep stage
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Per heart rate
Data science is used to create health statistics that can provide better personalized services so that users can share with external partners (such as health professionals and insurance companies) if they allow.
Chatbot (online customer support)
More and more websites use chat to provide customer support, but in many cases the person you are chatting with is a chatbot, not a person. Companies such as Ikea, hotels.com, and E.on use bots to filter out which customers they may need to contact. These bots use ml To identify information related to the text and provide possible answers to the query. If the bots are unable to provide the customer with the information they need, they are forwarded to human representatives. Duolingo, an app for learning new languages, uses chatbots to enable users to practice their newly learned language skills via text messages. They also use data science to collect and apply information about users ml Classify their personality and learning styles and the idea of assigning them to the chatbot that best matches them.
Recommended systems
Have you ever received an email from Amazon about products that interest you? Or have you seen the “Recommended” section of Netflix? These are two examples of recommended systems. These systems collect and preprocess data from activities within the site. for example
This data generates recommendations based on how your behavior is compared to other users on the site. Data Science allows you to group customers according to their actions and share recommendations between each group. So, if a few people with similar behaviors have seen a movie you don't have, Netflix recommends it to you.
And of course, there are many applications in professional contexts, including:
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Classification: Classify images containing vehicles, people, etc.
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Recognition: A common application is facial recognition
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Filtering: Get a large amount of images, videos, or documents and select one that contains a specific image, object, or reference
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Anomaly detection: For example, analyze large amounts of engine performance data to identify anomalies that could indicate a failure
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Prediction: For example, if you want to predict when your food is likely to get worse
The range of applications is growing very quickly, such as.
