The interest in Machine Learning for use in the various domains is expanding as the available amount of data increases with time. Machine learning proposes an abundance of techniques to extricate knowledge from data that can be rendered into purposeful objectives.

ML algorithms can reinforce the field information and automated function mostly related to regulation and optimization. Moreover, machine learning along with computer vision has augmented many domains where medical diagnostic, statistical data analysis and algorithms, scientific research, etc included. Such practices have already been done in the arena of smartphone applications, computer appliances, online websites, cybersecurity, etc.  

The extended data today is prevailing over multiple disciplines, obtaining interferences and valuable knowledge from data has appeared as the latest model of scientific inquiry as well as commercial application. In this blog, we will pick up some applications of machine learning implemented in our daily practices.

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Machine Learning Application in Daily Life

1. Search Engines

Search engines such as Google and Bing use machine learning algorithms to provide more accurate search results. Machine learning is used to improve the search engine's ability to understand natural language and provide relevant results based on a user's search query. Search engines use a wide range of machine learning algorithms, including natural language processing (NLP) and deep learning, to continuously improve their performance.

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2. Social Media

1. Facebook: While uploading a photo on Facebook, it automatically reflects faces and suggests friends tag. Facebook uses AI and ML to identify faces. Moreover;

• It uses the ANN algorithm that imitates the human brain and power facial recognition software.
• Facebook uses AI to personalize newsfeed and makes sure to reflect posts that entertain one.
• It shows ads of a particular business that are relevant to one's' interest.  

2. Pinterest: It employs computer vision to automatically recognize objects in the images or “pin” and then recommend similar pins. Other applications cover pam prevention, search, and discovery, email marketing, ad performance, etc with the help of machine learning.

3. Snapchat: It offers facial filters (known as Lenses) that filter and track facial activity, permits users to tag animated images or digital masks that shift when their faces move.  

4. Instagram: With the help of ML algorithms, sentiments behind the emojis can be identified. Instagram can make and auto-recommend emojis and emojis hashtags. There is the massive use of emoji across all demographics that are used to describe and explore by Instagram at a massive scale through emoji-to-text-translation.

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3. Smart Speakers

Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. You can even instruct assistants for certain tasks like “Set an alarm for 6 AM next morning”, “Remind me to visit Visa Office the day after tomorrow”.

Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.

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4. Mobile Apps

Machine learning is used in mobile apps for a variety of purposes, including image recognition, speech recognition, and natural language processing. Mobile apps like Snapchat and Instagram use machine learning algorithms to apply filters and effects to images and videos. Mobile apps like Siri and Google Assistant use machine learning to understand and respond to voice commands.

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5. Fraud Detection

Machine learning is used in fraud detection systems to identify and prevent fraudulent transactions. These systems analyze large amounts of data, including transaction history, user behavior, and external data sources, to identify patterns and anomalies that could indicate fraudulent activity. Machine learning is also used in cybersecurity to detect and prevent cyber attacks.

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6. Healthcare

Machine Learning incorporates a soup of techniques and tools to deal with the diagnostic and prognostic issues in the diverse medical realms.  ML algorithms are highly used for

• The analysis of medical data for detecting regularities in data,
• Handling inappropriate data,
• Explaining data generated by medical units,
• Also for effective monitoring of patients.

Machine learning also helps in estimating disease breakthroughs, driving medical information for outcomes research, planning and assisting therapy, and entire patient management. Along with machine learning, AI in healthcare is also implemented for efficient monitoring.

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Conclusion

In conclusion, machine learning has transformed the way we live our daily lives. From search engines and social media to smart speakers, mobile apps, fraud detection, healthcare, autonomous vehicles, online advertising, customer service, and education, machine learning is being used in many different ways. Machine learning algorithms are helping to improve accuracy, provide more personalized experiences, and increase efficiency. As machine learning continues to evolve and become more advanced, we can expect to see even more exciting applications in various industries. However, it is important to keep in mind the ethical implications of using machine learning, such as data privacy and potential biases in the algorithms. As we continue to develop and use machine learning, it is crucial to ensure that we are using it responsibly and ethically for the benefit of all.

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