These instructor-led live training courses, available both online and onsite, focus on supervised machine learning. Through interactive, hands-on sessions, participants learn to train models, generate predictions, and analyze data patterns with confidence.
NobleProg offers Supervised Learning as either an "online live training" or "onsite live training." The online format, also known as "remote live training," is delivered via an interactive remote desktop. Onsite sessions take place either on the client's premises in Athens or at NobleProg corporate training centers in Athens.
Supervised Learning is also referred to as Supervised Machine Learning.
Take Metro Line 3 (M3) from Airport toward Dimotiko Theatro.
Get off at Evangelismos.
The address is on Vasilissis Sofias Avenue, very close to the station. Evangelismos station is directly on the avenue.
Allow roughly 40–50 minutes total, depending on waiting/walking time.
From Athens Central Railway Station (Larissa Station)
Take the metro to Syntagma and change to Line 3 toward Doukissis Plakentias/Airport.
Get off at Evangelismos.
Walk to No. 46.
By bus
There are several buses running along Vasilissis Sofias Avenue. Nearby stops include Rigilis Street and Syntagma, while the Evangelismos area is served by multiple bus routes.
The metro is generally the simplest option, especially if you're arriving with luggage.
This instructor-led, live training in Athens (online or onsite) is designed for participants with diverse backgrounds who want to utilize Google's AutoML platform to develop customized chatbots for various applications.
Upon completion of this training, participants will be able to:
Grasp the fundamentals 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 in Athens (online or onsite) is aimed at intermediate-level professionals who wish to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to enhance production efficiency and reduce unexpected equipment failures.
By the end of this training, participants will be able to:
Implement AI models for predicting equipment failures in semiconductor manufacturing.
Analyze maintenance data to identify patterns and trends indicative of potential issues.
Integrate AI-driven predictive maintenance into existing manufacturing workflows.
Reduce downtime and maintenance costs through proactive equipment management.
This instructor-led, live training session in Athens (available online or onsite) targets beginner-level professionals seeking to understand and apply AI technologies in the semiconductor manufacturing sector.
By the conclusion of this training, participants will be able to:
Understand the fundamental principles of AI and their relevance to semiconductor manufacturing.
Identify key areas in semiconductor manufacturing suitable for effective AI implementation.
Utilize AI tools and techniques to enhance production efficiency and quality control.
Implement basic AI models to optimize manufacturing processes.
This instructor-led live training in Athens (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting.
By the end of this training, participants will be able to:
Learn to apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.
This instructor-led, live training in Athens (online or onsite) provides an introduction to the fields 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.
Use key models like neural networks and kernel methods for data analysis.
Implement advanced techniques for complex problem-solving.
Improve prediction accuracy by combining different models.
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