The human race has strived to create machines to reduce the workload on humans since time immemorial. The first industrial revolution was a direct result of the steam engine and the operational efficiency it brought. Today, we are witnessing machines with cognitive qualities – machines that can learn from experience, as if they had a brain of their own. This is the magic of machine learning – the process of teaching machines to enhance themselves through exposure to data. 

The concept of machine learning has been used in various capacities from the 1950s. Now, we bear witness to its absolute fruition as artificial intelligence takes the stage and marvels us with autonomous cars, and intelligent chatbots. Machine learning has come out of the closet and entered almost every business on the face of earth, thanks to the availability and affordability of data and superior computational powers. Let us see how different industries are using this technology.

Fraud detection for the financial institutions

Banks and financial institutes operate under the looming threat of fraudulent transactions and cyber crimes. There is risk involving loan defaulters, identity theft, borrowing money with fake collaterals among others. 

Machine learning algorithms can be used to find anomalies in transactional data to detect fraudulent transactions. Moreover predictive models involving machine learning can be used to identify probable defaulters based on historical data.

The banking and finance industry has played a pioneering role in terms of machine learning adaptation to counter both frauds and cyber criminals. It is also one of the largest users of analytics services, giving the analytics industry a strong forward-push.

Integrated applications for agricultural sectors

The thought of the agriculture sector comes to the mind whenever we talk about unpredictability in an industry. Agriculture, which is one of the most important parts of human civilization is subject to the whims of nature. A failing crop, a year without rain, a flood, a sudden change in temperature, everything can affect the yield and influence the lives of the people working in this industry.

Machine learning can bring a sense of security and predictability in this sector. It can be used to integrate soil data, weather forecasts, historical yield data, to recommend the ideal time of cultivation, the amount of fertilizers to be used, the type of crop, and other steps for a better harvest.

This sort of assisted agriculture can save farmers from uncertainty. There are in fact, automated devices to assess yield quality to help distributors decide a price for the crops. 

Machine learning in healthcare

Healthcare is the biggest global challenge at the moment and things could have been much worse without the help of machine learning. Let us see why.

Every connected medical device produces data. These data are critical to understand the patients’ response to different conditions. The ability to accumulate and analyze this information proves incredibly useful in terms of enhancing patient care.

Moreover, the patient reviews and transaction data at healthcare facilities can contain important clues as to the inconveniences faced by the patient parties. This information helps a healthcare facility improve customer service, maintain reputation, and reduce the monetary burdens on the patients.

All this data would have been very difficult to analyze without the help of machine learning algorithms.

Managing the supply chain

Machine learning adds great value to supply chain management in a bunch of different ways.

  • It allows automated maintenance alerts for machinery and vehicles.
  • The use of computer vision will greatly improve quality control.
  • Performing demand analysis and customer behaviour analysis helps the manufacturer make appropriate product modifications. 

From product design to distribution, every aspect of the supply chain can be augmented with the help of machine learning. It is one of the many reasons that has made machine learning so important for manufacturers.

Machine learning for marketing

Marketers have a much better visibility across their audiences across various demographic groups thanks to machine learning. Machine learning algorithms are based on various features including demographic data, transactional data, social media feeds, reviews, and ratings for customer based segmentation

Predictive models can even be used to draw an estimate about the returns of marketing investments. It makes a largely uncertain field look predictable and controllable.

To sum it up 

Be it manufacturing, healthcare, finance, or marketing, machine learning has gotten a really strong footing across industries. Topping your resume up with a machine learning online training can increase your employability quite significantly. Give it your best shot, practice consistent learning, and you should be headed towards great success.     

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