Machine learning
Make big data manageable!
Machine learning (ML) is a branch of artificial intelligence. By recognizing patterns in existing databases, IT systems are able to find solutions to problems independently.
The current status
A large number of machine learning implementation and development projects can currently be found. The deep learning sub-category in particular brings numerous new developments, e.g. in the areas of facial and speech recognition.
If you would like to start your own development project, we recommend using the Python programming language. The open source availability of numerous programming libraries such as TensorFlow from Google, scikit-learn or Theano enables the rapid integration and utilization of machine learning techniques.
Recognizing patterns in large amounts of data requires both high storage capacity and advanced knowledge in the field of machine learning. Cloud providers (Amazon, IBM, Google, Microsoft, etc.) now offer pattern recognition as “machine learning as a service” within their platforms.
Technology and use
Technology description
As the fourth industrial revolution progresses, more and more company and process-related data is being collected. The evaluation and assessment of these is therefore becoming increasingly difficult for people to handle. Based on existing databases, machine learning is used to identify regularities and patterns within the structures and thus enables the development of solutions and conclusions.
The subcategory of deep leaning also describes a method for generating artificial neural networks. These are modeled on a human brain in terms of their function and structure. By reading large data sets, important features can be automatically extracted and classified. Intelligent evaluation and predictions are therefore possible.
Image: Based on Towards Data Science
Possible application scenarios
There are currently numerous applications relating to machine learning. Machine learning can be used wherever large amounts of data need to be analyzed and searched for patterns.
In everyday life, the best-known applications are probably the recommendation services of Amazon and Netflix, the sorting of spam emails, voice recognition (Siri, Cortana) or facial recognition (Facebook).
In the production environment, machine learning can also be used in the areas of quality assurance, customer satisfaction, autonomous systems and predictive maintenance.
There are also initial areas of application in the office environment. With chatbots you can, for example conduct communication. You can make inquiries and receive corresponding information, recommendations for action or suggested solutions.
Step-by-step introduction
Step 1: Definition of the application scenario
Step 2: ML mapping
Step 3: Process data
A crucial step is to ensure that the data structure is suitable. Are you currently recording the correct data/characteristics?
Data exploration:
Based on your experience, carry out an assessment of the data to determine whether the data has been processed in such a way that it can be analyzed. This step is particularly important in order to gain an understanding of which categorizations of the data you should make and which input variables are required for the algorithms. If necessary, you can carry out initial investigations to determine which changes result from the variation of individual data.
Step 4: Modeling
Step 5: Evaluation
– Increase in data volume
– Clear categorization
– Reducing complexity
– Improving data quality
Opportunities for SMEs
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