Delivered either online or onsite, these instructor-led Supervised Learning courses provide an immersive, hands-on experience. Participants will learn to apply supervised machine learning techniques for model training, predictive analysis, and effective data pattern recognition.
Training options include "online live training" or "onsite live training." Online live training (also referred to as "remote live training") utilizes an interactive remote desktop environment. Onsite live training is available at your local premises in Copenhagen or within NobleProg corporate training centers in Copenhagen.
This discipline is also recognized as Supervised Machine Learning.
You can also take bus 26 or 7A and get off around Vesterbros Torv; from there it is a short walk.
By bus
Useful nearby stops include:
Vesterbros Torv (Vesterbrogade) — about 1 minute from the area.
Frederiksberg Allé (Vesterbrogade) — nearby.
Trommesalen (Vesterbrogade) — nearby.
Bus 26 and 7A serve the Vesterbrogade corridor.
København, Copenhagen
NobleProg København, Bredgade 37, København K, denmark, 1260
Near Amalienborg Palace & Royal Theatre.
If you arrive by train
If you arrive at Copenhagen Central Station (København H):
Walk to København H Metro.
Take the M3 or M4 metro toward Kongens Nytorv.
Get off at Kongens Nytorv.
Walk north toward Nyhavn, then continue along Bredgade.
Bredgade 37 is about 500 m / 6 minutes' walk from Kongens Nytorv.
Another easy option
Marmorkirken (Marble Church) Metro Station is also very close to Bredgade 37. The nearby Bredgade area is about 2 minutes' walk from Marmorkirken station.
By bus
There are bus stops around Dronningens Tværgade / Bredgade, so you can also travel by bus and walk the final few minutes.
This instructor-led, live training in Copenhagen (online or on-site) is intended for participants with varying levels of expertise who aim to utilize Google's AutoML platform to construct customized chatbots for diverse applications.
By the end of this training, participants will be equipped to:
Comprehend the basics of chatbot development.
Navigate the Google Cloud Platform and access AutoML.
Prepare data for training chatbot models.
Train and evaluate custom chatbot models using AutoML.
Deploy and integrate chatbots into various platforms and channels.
Monitor and optimize chatbot performance over time.
This instructor-led, live training delivered in Copenhagen (online or onsite) is tailored for intermediate professionals seeking to leverage AI-driven predictive maintenance in semiconductor manufacturing. The objective is to boost production efficiency while minimizing risks associated with unforeseen equipment failures.
By the end of the program, attendees will have the capability to:
Implement AI models designed to predict equipment failures in semiconductor contexts.
Analyze maintenance datasets to detect patterns and trends that indicate emerging problems.
Integrate AI-powered predictive maintenance into current manufacturing operations.
Decrease downtime and reduce maintenance costs via proactive equipment oversight.
This live, instructor-led course, available online or on-site in Copenhagen, is tailored for beginner-level professionals looking to comprehend and implement AI technologies in the semiconductor manufacturing field.
After completing this training, participants will be able to:
Understand the foundational principles of AI and their relevance to semiconductor manufacturing.
Recognize key areas in semiconductor production suitable for effective AI application.
Leverage AI tools and techniques to boost production efficiency and quality control.
Apply basic AI models to refine and optimize manufacturing processes.
This live, instructor-led training in Copenhagen (available online or on-site) is tailored for intermediate-level data analysts aiming to master the use of RapidMiner for value estimation and projection, as well as for employing analytical tools in time series forecasting.
By the end of this program, participants will have the capability to:
Implement the CRISP-DM methodology, select the most appropriate machine learning algorithms, and refine model construction and performance.
Utilize RapidMiner for estimating and projecting values, effectively deploying analytical tools for time series forecasting.
This instructor-led, live training in Copenhagen (available online or onsite) provides an introductory overview of pattern recognition and machine learning. It covers practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
By the end of this training, participants will be able to:
Apply core statistical methods to pattern recognition.
Utilise key models, such as neural networks and kernel methods, for data analysis.
Implement advanced techniques for solving complex problems.
Improve prediction accuracy by combining different models.
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