What is machine learning?

Machine learning is a set of artificial intelligence techniques that gives web and mobile applications the ability to learn, adapt, and improve over time. It does this by processing vast amounts of data, identifying trends and patterns within it – most of which would not be apparent to a human being – and then making decisions and taking actions to help meet specific objectives.

Why do you need machine learning?

Machine learning solutions open your business up to a wide variety of new opportunities. You can use machine learning models to personalize your customer experience, automate processes, gain deeper insights with advanced analytics, and deploy digital solutions that will change the way customers interact with your product.

Machine learning is widely applied to business problems, reducing costs and increasing customer satisfaction. ML algorithms can be used in applications across practically any industry or sector – from eCommerce to finance, healthcare to education, and cybersecurity to charity services.

What are the best examples of machine learning?

Machine learning solutions are being used in various business sectors – both B2B and B2C companies can benefit from it.

Amazon uses an ML-powered recommendation engine that drives 
35% of its total sales. Thanks to the AI-Bot Harry, AXA saves roughly 17,000 man-hours a year. At the same time, Vodafone noticed a 68% improvement in customer satisfaction after introducing its machine learning chatbot TOBi.

American Express and PayPal use machine learning models to quickly analyze millions of transactions and data points, giving them 
real-time fraud detection capabilities. These advanced digital tools allow customers to resolve problems with suspicious transactions almost instantly.

Researchers based at UCLA managed to 
identify cancer cells with greater than 95% accuracy after equipping a special microscope with machine learning algorithms.

Where can ML solutions be used?

Machine learning models are used in a range of industries. Businesses are using models to improve performance through process automation, predictive analytics, and anomaly detection, among a variety of other use cases.

For example, eCommerce and marketing leverage ML algorithms for their recommendation engines to provide better customer experiences. Hedge funds use ML tools to forecast stock prices, while insurance companies use advanced techniques to calculate risk more accurately. Banks and other financial institutions are able to detect suspicious transactions using fraud detection models. Medical companies use digital tools and deep learning approaches to diagnose medical conditions based on sets of symptoms.

Are machine learning services right for your business?

Machine learning projects are often high-risk projects due to their complex dependencies on data. That is why top companies offering machine learning services conduct feasibility studies to reduce the risk before engaging in a project. In this way, they ensure that sufficient data is available and that predicted outcomes are in line with the project goals.




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