Whether delivered online or on-site, our instructor-led live training in Supervised Learning employs interactive, hands-on exercises to demonstrate the effective application of supervised machine learning techniques for training models, generating predictions, and analysing data patterns.
These courses are available as either “online live training” or “on-site live training.” Online live training (also referred to as “remote live training”) is facilitated through an interactive remote desktop environment. On-site live training can be conducted locally at your premises in Gaia or at NobleProg corporate training centres in Gaia.
Supervised Learning is also widely recognised as Supervised Machine Learning.
NobleProg -- Your Local Training Provider
Holiday Inn Porto - Gaia
220, Rua Diogo Macedo, 220, Vila Nova de Gaia 4400-107 , Gaia, Portugal
From Porto by Metro
If you're coming from Porto city centre:
Take the Metro Line D (yellow line) toward Vila d’Este.
Get off at João de Deus.
From João de Deus, walk about 13 minutes to: Rua Diogo Macedo 220, 4400-107 Vila Nova de Gaia.
From Vila Nova de Gaia train station
If by “local train station” you mean Vila Nova de Gaia-Devesas:
The address is approximately 12 minutes walking from Vila Nova de Gaia station according to the accommodation's location information.
You can therefore walk directly rather than taking the metro.
This instructor-led, live training in Gaia (online or onsite) is designed for participants with varying levels of expertise who want to use Google's AutoML platform to build custom chatbots for various applications.
By the end of this training, participants will be able to:
Understand 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 optimise chatbot performance over time.
This instructor-led, live training in Gaia (online or onsite) targets intermediate-level professionals who wish to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to enhance production efficiency and reduce unexpected equipment failures.
Upon completing this training, participants will be able to:
Deploy AI models to predict equipment failures in semiconductor manufacturing.
Analyze maintenance data to uncover patterns and trends that signal potential issues.
Integrate AI-driven predictive maintenance into current manufacturing workflows.
Decrease downtime and maintenance costs via proactive equipment management.
This instructor-led, live training in Gaia (online or onsite) is aimed at beginner-level professionals who wish to understand and apply AI technologies within the semiconductor manufacturing industry.
By the end of this training, participants will be able to:
Understand the basic principles of AI and how they apply to semiconductor manufacturing.
Identify areas within semiconductor manufacturing where AI can be effectively implemented.
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 Gaia (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilise analytical tools for time series forecasting.
By the end of this training, participants will be able to:
Apply the CRISP-DM methodology, select suitable machine learning algorithms, and improve model construction and performance.
Utilise RapidMiner to estimate and project values, employing analytical tools for time series forecasting.
This instructor-led, live training in Gaia (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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