Artificial intelligence, machine learning and data analysis are among the pioneering driving forces of the digital revolution in the 21st century.
Data is the raw material of the future, which increasingly determines competitive advantages and, unlike traditional raw materials, is not used up.
In the data age, a large part of the world's population is networked and communicates with machines, cars, houses and other devices on the Internet of Things (IoT).
Unfortunately, about 90% of currently available data is still unstructured or partially structured, which prevents or reduces its usability for machine learning.
Therefore, it is all the more important for companies to adjust data acquisition to the collection of useful semantic data in good time in order to avoid wasting valuable capital and to save the high costs of subsequent manual semantification.
Artificial Intelligence (AI)
Artificial intelligence is the generic term for intelligence services that were previously reserved for humans.
"Machine Learning", "Deep Learning", "Natural Language Processing" (NLP) and "Neural Networks" are sub-areas of artificial intelligence or "Artificial Intelligence (AI)".
Machine Learning (ML)
Machine learning describes mathematical techniques that enable a system (machine) to independently generate knowledge from experience.
Deep learning and artificial neural networks
"Deep Learning" is a sub-area of machine learning. Sometimes the terms "deep learning" and artificial neural networks are used synonymously.
"Deep Learning" works with particularly deep artificial neural networks in order to achieve very effective learning success with the help of large amounts of data. Using neural networks, the machine enables itself to recognize structures, to evaluate this recognition and to improve itself independently in several forward and backward passes of the adaptation of links.
The neural networks are divided into several layers.
"Deep Learning" relies on statistical data analysis and not on a deterministic algorithm. Statistical data analysis is required whenever there are no clear rules for linking data, such as in image recognition (classification of image data) or analysis of audio and video data.
The application possibilities and areas of artificial intelligence range from prognosis, image recognition, face recognition, anomaly recognition, pattern recognition, recommendation services, etc. up to the optimization of almost all conceivable processes.
Our workshop is intended to give your employees a comprehensible insight into the potential of data, its analysis and forecasting with the help of machine learning.
Don't miss out on the latest developments in digitization. Learn everything about data acquisition, processing, analysis, visualization, reduction, modeling, and forecasting.
After a general overview and insight into essential theoretical basics, we will examine your personal data situation with you and point out possibilities for optimising acquisition and exploitation so that you can fully exploit your potential.
We offer workshops in the following areas:
Data Science, ML and AI
- Overview and History
- knowledge discovery
- Big Data (Mining and Visualization)
- KI applications (robots, sensors, inference)
- Machine vision (image recognition, video analysis, etc.)
- Speech and text (speech analysis and synthesis)
- computational linguistics
- recommendation services
- time series analysis
- characteristic analysis
- Observed learning (regression, classification, etc.)
- In-depth learning (neural networks, transfer learning, etc.)
- Unobserved learning Cluster analysis, dimension reduction, etc.)
- Partially supervised learning (active learning, feedback, etc.)
- Encouraging learning (game theory, Markow decision problem, etc.)
The composition and depth of the workshop can be individually arranged.
Active Learning for semantification of unstructured data:
How to use your unstructured data for machine learning and AI applications
- ML and KI applications
- Unstructured data
- Semantification through annotation and labeling
- Active Learning
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Today, recommendation services are a must for your business applications in a wide variety of application areas.
Lower price offer recommender:
Determine the lowest price provider.
Next best offer:
Recommend similar products from different vendors.
Targeting and personalization of content and product recommendations strengthen customer loyalty, brand value and sales.
Next step recommender:
Optimize and accelerate workflows through AI-driven decision making.
Predicting and optimizing sizes, events and processes is an essential tool for increasing performance and sales.
Customer churn prediction:
Based on your data, recognize which of your customers could potentially cancel subscriptions and services or where and how you can and should influence decisions.
Improve delivery processes and troubleshooting with feedback data.
Search and retrieval of information are an integral part of most systems in industry and services.
Integrate a user-defined search in selected databases or on the Internet into your application.
Information retrieval (IR):
Benefit from intelligent machine learning algorithms for highly selective information retrieval.
Natural Language Processing (NLP):
Use up-to-date automated language and word processing techniques to integrate information about people, places, events, opinions, etc.
The calculation of similarities and anomalies in large data sets helps to eliminate redundancies and minimize error susceptibilities as well as security risks in many use cases.
Similarity calculations are the basis of recommendation services and optimization processes.
Anomalies in data can provide insightful information to uncover problems and development potential or combat threats in real time.
Holen Sie alles aus ihren Daten! Wir untersuchen Ihre Daten auf Redundanzen und Verwertbarkeit und gewinnen selbst aus unstrukturierten Daten wertvolle Erkenntnisse, um Ihr Business zu optimieren.
Lassen Sie uns aus Big Data Smart Data machen.
The image recognition of products via mobile app, integrated into your webshop, offers the possibility that users can find specific products whose article description is unknown to them.
The procedure is not only more convenient for the user, but often also superior to a simple text search, which provides unwanted or superfluous information.
We construct for you a model tailored to your data, a neural network, which is trained with deep learning or transfer learning and can be continuously improved and expanded by feedback from the user.