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Applying Machine Learning to Daily Life A Brief Insight on Machine Learning Engineers – By Lilian Xu

Applying Machine Learning to Daily Life A Brief Insight on Machine Learning Engineers – By Lilian Xu

Machine learning engineers are becoming increasingly sought after as the volume of data being stored and processed electronically grows exponentially. Presently, one-third of surveyed companies in the United States already use artificial intelligence and machine learning. More than half of all managers surveyed predicted that their use of artificial intelligence and machine learning will increase within three to five years. One reason machine learning programs are so widely implemented is the extreme inefficiency of analyzing large quantities of company data by hand. Using an intelligent program guarantees more efficient analysis as well as lower rates of error. The job of machine learning engineers is to design and code intelligent programs that are able to run independently and adapt. Training a machine learning program by feeding it large sets of clean (readable) data will result in the program accumulating information and making improvements in its accuracy each time it is used.

Although there are so many different types of machine learning, they all have relatively similar job requirements. So far, the machine learning field is quite new and still undeveloped. The rough job description includes having a degree in statistics, math, or computer science. Also, it is a requirement to have experience in both data analysis and programming (think Python or Java).

Practical Machine Learning Applications

The ongoing rapid rise of machine learning is mainly due to its versatile nature and the multitude of machine learning applications. Here are examples of the most common machine learning applications that people use everyday: social media services, fraud or malware screening, face and voice recognition, and video surveillance. Additionally, machine learning is used by companies to make a wide variety of predictions. These predictions include GPS traffic predictions, the storing of data for relevant advertisements, and video/movie recommendations on sites like YouTube or Netflix.

Firstly, social media services like Instagram and Tiktok use algorithms that give each person a highly specialized stream based on the hashtags of posts a user has liked in the past. Also, they recommend “accounts to follow” based on the profiles a user is already following. Next, Paypal utilizes machine learning codes to recognize and prevent illegitimate monetary transactions. It easily pinpoints the differences of each transaction and flags transactions that stand out from the overall pattern. Likewise, Gmail’s “Spam” folder detects common structures of emails containing malware or spam and effectively keeps those types of emails outside of your main inbox. These last two methods mentioned (fraud and malware/spam email detection) are extremely flexible and can be useful to any type of company. Machine learning also powers face recognition used for Apple Face ID and voice recognition used for virtual assistants like Siri and Amazon Alexa.

Furthermore, while it is difficult for a human being to keep track of dozens of security monitors, machine learning programs can easily monitor security cameras to detect suspicious or odd behaviors. Additionally, GPS navigation is made possible through the collection and storing of the vehicles’ coordinates, as well as the prediction of traffic by using traffic data from the past. Machine learning is the reason you get relevant advertisements that are related to your Google searches. In other words, you might get pop-up ads about fashion a couple hours after visiting a clothing store’s website. Though these aspects seem relatively insignificant, machine learning programs make each and every person’s daily interactions with technology more convenient.

The Future of Machine Learning

These types of machine learning applications are just the tip of the iceberg. Moreover, adding on to the numerous types already mentioned, there are always new types of practical machine learning applications that are being developed. For instance, many companies (like Tesla) are working on the development of self-driving cars. Because the future will most likely depend heavily on new machine learning inventions, basic machine learning skills are desired for a wide range of jobs. The countless different types of machine learning ensures that the machine learning career path will only expand and lead to more and more opportunities as the world of tech continues to progress.

Works Cited

Analytics, Potentia. “Potentia Analytics.” Potentia Analytics Inc., Potentia Analytics Https://Www.potentiaco.com/Wp-Content/Uploads/2019/07/Potentia-Analytics-Logo_TM_AI-for-Healthcare_transparent-2-2.Png, 18 Dec. 2019, www.potentiaco.com/what-is-machine-learning-definition-types-applications-and-examples/.

Half, Robert. “How to Become a Machine Learning Engineer.” Machine Learning Engineer Job Description and Salary, Robert Half, 21 Jan. 2020, www.roberthalf.com/blog/the-future-of-work/how-to-become-a-machine-learning-engineer.

Software, Daffodil. “9 Applications of Machine Learning from Day-to-Day Life.” Medium, App Affairs, 30 Nov. 2017, medium.com/app-affairs/9-applications-of-machine-learning-from-day-to-day-life-112a47a429d0.

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