Make a clean breast of Machine Learning


What is machine learning?

The simplest machine learning definition? The practice of teaching a computer how to spot patterns and make connections by showing it a massive volume of data. So rather than programming software to accomplish a specific task, the machine uses Big Data and sophisticated algorithms to learn how to perform the task itself. Machine learning allows applications to “think” and independently make a determination or prediction – going beyond what predictive analytics and Big Data analytics can do, and often beyond what humans can do. A popular consumer example of machine learning is a recommendation engine in an online retail environment.

What is deep learning?

Deep learning, sometimes known as cognitive computing, is a form of advanced machine learning. It uses multi-layered (aka deep) neural networks to simulate human thought processes. These networks are made up of small computing nodes that mimic the synapses of the human brain. Using input data sets and sophisticated algorithms, machines can help solve complex, non-linear problems. Deep learning is responsible for breakthroughs such as speech and image recognition and natural language processing. Some popular deep learning examples include:
  • Facial recognition software
  • Self-driving cars
  • Smart home automation devices

What is supervised learning?

There are three main ways that machines can “learn”:
  • Supervised learning – in this approach humans label the inputs and outputs and then the model figures out the rules for connecting the two.
  • Semi-supervised (or reinforcement) learning – the machine is
    rewarded or penalized for actions it takes through trial and error, and the algorithm adjusts accordingly.
  • Unsupervised learning – algorithms are left to discover patterns in the data (which is sometimes clustered) on their own.
Regardless of the type of training used, the machine is able to learn from the data on its own, absorbing new behaviors and functions over time. The result is a model which can be used to predict outcomes based on data, and which is regularly retrained for accuracy.

Welcome to the future of business

See how machine-based neural networks can understand a billion pieces of data in seconds – and place the perfect solution at decision makers fingertips. This is the power of SAP Leonardo Machine Learning for business.

Benefits of machine learning

 Faster decision making

Machine learning can automate and prioritize routine decision making processes – so you can achieve best outcomes sooner. For example, when coupled with the Internet of Things, it can help you decide what to fix first in your manufacturing plant.


Your data is constantly being updated, which means your machine learning models will be too – much faster than humans can currently develop them. This lets you quickly discover and process new insights to adapt to rapidly changing business environments.

 Innovation and growth

 An “algorithmic business” uses advanced algorithms to drive process automation and improved decision making. Making the shift can accelerate overall knowledge harvesting and pave the way for innovative business models, products, and services.

 Unique insights

One of the most exciting uses for machine learning is to understand patterns in Big Data in a way that humans currently can’t – and then trigger concrete actions. For example, it can predict potential sales opportunities and then recommend actions to close deals.

 Business acceleration

With machine-aided business processes and faster overall workflows, you can optimize business operations and your product and service offerings – so you can do and sell more while lowering back-office costs and TCO.

 Better outcomes

AI and machine learning help to eliminate human error, improve the quality of outputs, and bolster cybersecurity – a must for financial service and other companies that need to protect sensitive information and comply with regulations.

Machine learning use cases

The most popular examples of machine learning in action are consumer applications like recommendation engines and smart devices. But the technology also holds great promise for business-to-business (B2B) use cases. Here are two key areas we think the technology will really shine:

Smart business processes

Machine learning shifts traditional rules-based processes    to intelligent ones that can discover new patterns in large, unstructured data sets and make strategic predictions all     on their own. It can also take on highly repetitive tasks    such as checking invoices and travel expenses for accuracy.

Digital assistants and bots

Advances in AI technology suggest that self-learning algorithms may soon come to their own conclusions within certain parameters and develop context-sensitive behavior. Devices will be able to schedule meetings, translate documents, and take on other routine business tasks.


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