Machine learning allows computers to calculate extreme amounts of data and it’s currently one of the quickest growing sectors in the tech world.
Thanks to new computing technologies, machine learning today is not like machine learning a few years back.
Researchers working in artificial intelligence wanted to see if computers could learn from large amounts of data and hence Machine Learning was born.
The system learns from earlier data to produce reliable, decisions, and results.
Today these ML systems have the ability complex mathematical calculations to big data at computational speed.
You can take an example of Google’s self-driving car itself. Also, the online recommendation you get from Amazon and Netflix are a part of a larger ML system. It mainly helped in fraud detection and to know what customers are saying about you on social media.
Jobs in machine learning are becoming highly sought after, with big tech organizations offering huge salaries to ML engineers. Google offers an average salary of $100000/year to ML engineers.
The demand for machine learning engineers is growing every year. You need to do is to work on your skills. Learning ML will not be easy, but with the right mindset, it’s easy. I suggest you take a good course like Udacity’s machine learning nanodegree as it covers all the basic concepts of ML.
Why is machine learning important?
This can be attributed to growing volumes of available data, an increase in computational power, and affordable data storage.
This means that now it’s easy to automatically produce models that can analyze bigger, more complex data and deliver fast and accurate results.
And by building precise models, companies have a better chance of identifying market opportunities to get more profit out of it.
Who’s using it?
Most of the major industries have realized the potential of ML and use it to simplify a large amount of data.
With this, they are able to work more efficiently or gain an advantage over competitors.
Here are a few sectors that make use of Machine Learning:
Financial services
In order to identify important insights in data and prevent fraud, banks today, use ML in some form.
It helps them to find investment opportunities, or help investors know when to trade. The use of data mining help to minimize the risk of fraud.
Health care
Health care use machine learning to know a patient’s health in real-time using wearable devices and sensors. It also helps experts analyze data to identify trends that may lead to improved diagnoses and treatment.
E-Commerce
The recommendation system build by Amazon is a perfect example of Machine Learning in e-commerce.
Based on one’s buying history the algorithm suggests you the most relevant products.
They rely on machine learning to capture data, analyze it, and use it to enhance your shopping experience.
Mobility
Companies like Uber use it to identify patterns and trends is key to transportation, to suggest a more reliable route based on traffic analysis and road conditions.
This also helps delivery companies to predict peak hours and manage accordingly to profitability.
Hence we think machines learning has deep implications in our daily life and also on our future. Along with this, we have studied its advantages and application in different fields which are helping us in our real life. As a result machines learning will become more trending in the near future.
