Delivered as instructor-led live training through online or onsite sessions, this course provides an interactive, hands-on exploration of supervised machine learning techniques. Participants will learn to train effective models, generate reliable predictions, and analyze data patterns with confidence.
Supervised Learning training is offered as either "online live training" or "onsite live training." Online live training, also referred to as "remote live training," is facilitated through an interactive remote desktop. Onsite live training is conducted either locally at your premises in Prague or within NobleProg corporate training centers in Prague.
Supervised Learning is also commonly referred to as Supervised Machine Learning.
From Prague Main Train Station (Praha hlavní nádraží)
Take tram 9 from Hlavní nádraží toward Sídliště Řepy.
Get off at Újezd.
Walk about 10 minutes toward Malostranské náměstí / Prokopská.
Continue along Prokopská to 296/8.
Alternative: Take the metro C from Hlavní nádraží → Muzeum, change to metro A → Malostranská, then walk across Malá Strana. This involves more walking.
From Prague Bus Station — Florenc
Take metro B from Florenc toward Zličín.
Get off at Můstek.
Change to metro A toward Nemocnice Motol.
Get off at Malostranská.
Walk approximately 10–15 minutes to Prokopská 296/8.
This instructor-led live training in Prague (online or onsite) is aimed at participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.
By the end 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 Prague (online or onsite) targets intermediate-level professionals who aim to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to improve production efficiency and mitigate unexpected equipment failures.
By the end of this training, participants will be able to:
Deploy 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 in Prague (online or onsite) targets beginner-level professionals eager to comprehend and apply AI technologies within the semiconductor manufacturing sector.
Upon completion of this training, participants will be equipped to:
Grasp the fundamental principles of AI and their application to semiconductor manufacturing.
Pinpoint areas in semiconductor manufacturing where AI can be effectively deployed.
Utilize AI tools and techniques to improve production efficiency and quality control.
Implement basic AI models to optimize manufacturing processes.
This instructor-led live training in Prague (online or onsite) targets intermediate-level data analysts who want to learn how to use RapidMiner for value estimation and projection, and to utilize analytical tools for time series forecasting.
By the end of this training, participants will be able 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 Prague (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on 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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