What is Machine Learning?
The simplest machine learning definition
Introduction to machine learning
Machine learning uses sophisticated algorithms to “learn” from massive volumes of Big Data. The more data the algorithms can access, the more they can learn. Real-world machine learning examples are everywhere. Think of personalized product recommendations on Amazon, facial recognition on Facebook, or fastest route suggestions in Google Maps.
What is a neural network?
Neural networks – aka artificial neural networks – are a type of machine learning that is loosely based on how neurons work in the human brain. They are computer programs that use multiple layers of nodes (or “neurons”) operating in parallel to learn things, recognize patterns, and make decisions in a human-like way.
What is deep learning?
Deep learning is a “deep” neural network that includes many layers of neurons and a huge volume of data. This advanced type of machine learning can solve complex, non-linear problems – and is responsible for AI breakthroughs such as natural language processing (NLP), personal digital assistants, and self-driving cars.
Supervised vs. unsupervised learning
Supervised learning algorithms are trained using data that includes the correct answers. They build models that map the data to the answers – and then use these models for future processing. Unsupervised algorithms learn from data without being given the correct answers. They use large, diverse datasets to self-improve.
Machine learning use cases in key sectors
Manufacturing
Manufacturers collect a huge amount of data from plant sensors and the Internet of Things – which is perfect for machine learning. Computer vision and anomaly detection algorithms are used for quality control – and others are used
for everything from predictive maintenance and demand forecasting to powering new services.
Finance
Few industries are better suited for machine learning than finance – given its high data volumes and historical records. Algorithms are used for trading stocks, approving loans, detecting fraud, assessing risks, and underwriting insurance. They’re even used for “robo advising” customers and aligning portfolios to user goals.
Healthcare
Machine learning algorithms can process more data and spot more patterns than any team of researchers or doctors, no matter how many hours they put in. From medical image analysis and early cancer detection, to drug development and robot-assisted surgery – the machine learning possibilities in healthcare are endless.
Machine learning research
We’ve built a global partnership network with top universities such as MIT, Stanford, NYU, and the University of Amsterdam to explore the future of machine learning and advance the technology for business. Through this collaboration, we focus on a variety of machine learning research topics – and work on solving open AI challenges in a range of industries. This large pool of expertise helps us keep pace with the latest machine learning trends and deliver new techniques in the context of SAP solutions.
Stay on top of machine learning trends
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Bringing transparency into AI
There is a growing demand to understand how AI and machine learning models and algorithms work, especially when there is an expanding number of machine learning cases without humans in the loop.
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Designing AI for people
The idea that a non-biological creation can learn, solve complex goals, and flourish in our world is a mighty leap forward. Where will it take us? How will we steer this emerging powerhouse that so many in the world are creating?
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Three ways AI will transform customers’ experience
Retailers are exploring ways to improve the customer experience across all engagement points, including marketing, buying, and after-sales service. Discover the strategies and AI tools they’re using.