Examples of machine learning application

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    You've probably heard the term "Machine learning" or "Artificial Intelligence" lately. It is going to turn our world completely upside down and 50% of today's jobs will become obsolete. Or will it? In any case, we at SevenLab find it a very interesting development. Hence this article!

    Recently I have been on LinkedIn wrote an extensive article about how AI works with a focus on machine learning. In a nutshell, machine learning allows you to predict, discover and detect.

    During my own research, I made a list of concrete and interesting examples. Below an overview. Leave your email to see the extended list.

    PR and (video) content companies


    • How much an online article will be read, even before you post it (relationship content keywords, time of year, historical readership numbers etc.);
    • How many viewers will watch video content (perhaps related to media budget, broadcaster, target group, content, etc.)



    • when you should do proactive maintenance
    • Capacity utilised
    • required fuel
    • How many refreshments are to be taken on board (perhaps related to date, temperature/weather, number of people in flight/travel);
    • budget per passenger
    • the amount of tickets that will be booked in order to determine the best possible price (number of visitors to the site, time of year, price of destination, marketing budget)


    • Marketing intelligence;
      • Predicting to whom you should send certain marketing campaigns -> to whom you have the most chance of success and/or who delivers the most;
      • Sentiment analysis for social media
      • Automated product tagging with image recognition
      • Predicting stock requirements
    • Predict what a visitor is most likely to buy (history, demographics, price, region, roi)
      • Predicting the best price
      • Fraud detection with transactions
      • Shipping before a product is purchased (or partially)



    • volume/spending per sales channel;
    • the crowds on the logistics channel;
    • required capacity;
    • Travel time (may be weight, region, average travel time, suply chain third party variables, etc.)

    Accounting and administration

    • Forecast financial data for customers, based on more than just accounting;
    • Detecting fraud and/or errors in administration



    • Energy consumption of individual houses / required capacity -> purchasing;
    • efficiency of renewable energy;
    • Fraud detection (where are the cannabis plantations, how big is the chance that a customer does not pay?)



    • required maintenance per object
    • best object price;
    • capacity of object/space;
    • energy consumption per object



    • required stock
    • necessary materials field service



    • required workforce on a specific day in the future;
    • sickness reports per day, individual level;
    • Customer demand (seasonal work, pipeline);
    • required cash flow;
    • staff availability


    • Predicting how people move through the game world
    • Predicting number of subscriptions