Written by Mohamed Ali Habib

What is Machine Learning?

You may have heard of machine learning (ML), but you want to understand how really beneficial it can be to your business without getting overwhelmed by the technical details that definitions may convey, you have come to the right place.

ML is the science that provides machines with the capability of learning automatically, acting, and improving from their experience without being explicitly programmed. Data-rich businesses can make use of ML to learn from data, identify patterns, and extract insights that may be of interest or even hidden. This helps companies make crucial and strategic decisions based on answers to questions.

How ML is used in today’s business?

The breakthroughs in machine learning research and its applications, availability of data, faster processing

power—​ especially GPUs, and affordable means of local and cloud data storage all have helped to develop outstanding ML applications. There is a plethora of examples of how ML is being applied to business problems but here’s a few of its applications:

● Autonomous Driving: Self-driving vehicles is among the hottest and most sophisticated applications of machine learning. Though still in its early stages, many companies like Tesla, Google, Uber, and other tech and automobile manufacturers are developing various self-driving technologies and building their own prototypes. Self-driving vehicles can improve our transportation systems by bringing more safety to roads which may lead to saving more money by potential reduction in cost of insurance. Environmental benefit is a big plus and the list of benefits goes on. All of that could not have been done without machine learning techniques because things like detecting and identifying objects, object localization, and prediction of movement could have been nearly impossible to program explicitly​—because, for example, you cannot predict all the movements of a pedestrian or another car.

● Healthcare: there are some fascinating applications of machine learning that are transforming the healthcare industry today. Cancer diagnosis, detecting Alzheimer’s disease, and identifying tuberculosis are examples of disease detection. Along with that, robot-assisted surgery is helping to perform more precise procedures using machine learning.

● Recommendation Systems:​ machine learning is being used to improve shopping experience by offering personalized recommendations to users. Companies like Netflix are using recommendation systems to help viewers find shows that fit into their tastes to increase their profits. Amazon is another example, as it hires machine learning engineers whose job it is to develop a product recommendation system in order to sell more products.

● Fraud Detection:​ fraud detection process is being enhanced by applying machine learning algorithms in order to build intelligent fraud detectors. For example, companies are separating legitimate transactions from those fraudulent ones.

● Stock Prices Prediction: predicting the stock market prices is very difficult to achieve because there a lot of factors that can affect the prices. Machine learning techniques are being used to build intelligent models that can make predictions on future prices increasing profit for traders.

Conclusion:

Machine Learning is enhancing many aspects of business and making tremendous gains for society as a whole. It’s always evolving and there’s a lot to learn as Dave Waters said “A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.”, when you realize that we are still in the early stages, you get a sense of how beneficial machine learning is  since it is already leading to significant improvements. If you are a business executive, a CEO, or an entrepreneur, you should consider thinking about how machine learning can applied to improve your business.

The race to building AI systems is compelling and urgent and no one wants to fall behind. It’s about starting to use it and develop experiences that will be valuable along the way. Who knows where machine learning will be in ten years from now and what it will be able to achieve, it’s questions like these that will be thrilling to answer.