Delivered either online or onsite, our instructor-led live Supervised Learning courses provide interactive, hands-on experience. Participants will learn to effectively employ supervised machine learning techniques for model training, prediction generation, and the analysis of complex data patterns.
We offer Supervised Learning instruction in two formats: "online live training" and "onsite live training". The online live option, also referred to as "remote live training," utilizes an interactive remote desktop platform. Onsite live training is conducted either at your local premises in Bangkok or within NobleProg corporate training centers in Bangkok.
Please note that Supervised Learning is also widely recognized as Supervised Machine Learning.
NobleProg -- Your Local Training Provider
Empire Tower
1, South Sathorn Road,, Bangkok, thailand, 10120
Ways to get there
BTS Skytrain: Take the Silom Line and get off at BTS Chong Nonsi. The Empire is within walking distance; the building has access from the Sathorn/Naradhiwas area.
BTS Saint Louis:Saint Louis station is also nearby, around a 7-minute walk according to current location information.
BRT: Get off at BRT Sathorn. It's approximately a 2–4 minute walk to the building.
MRT:MRT Silom is another option, followed by a walk toward Sathorn.
Taxi/Grab: Give the driver: “The Empire, 1 South Sathorn Road, Sathon, Bangkok 10120.” The official building site confirms this address.
By car: The building can be accessed from Sathorn Road or Naradhiwas Rajanagarinda Road.
This live, instructor-guided training session in Bangkok (online or onsite) caters to participants from diverse professional backgrounds who aim to utilize Google’s AutoML platform to construct customized chatbots for a wide range of applications.
By the end of this session, participants will be equipped to:
Grasp the core principles of chatbot engineering.
Explore the Google Cloud Platform ecosystem and utilize AutoML.
Curate and prepare data for training chatbot models.
Train and assess custom chatbot models using AutoML.
Integrate and deploy chatbots across multiple platforms and channels.
Track and refine chatbot performance for sustained effectiveness.
This instructor-led, live training in Bangkok (online or onsite) is tailored for intermediate-level professionals seeking to leverage AI-driven predictive maintenance in semiconductor manufacturing to boost production efficiency and curb unexpected equipment failures.
By the end of this training, participants will be able to:
Deploy AI models to forecast equipment failures in semiconductor manufacturing.
Analyze maintenance data to detect patterns and trends that indicate potential issues.
Integrate AI-driven predictive maintenance into existing manufacturing workflows.
Lower downtime and maintenance costs through proactive equipment management.
This instructor-led live training (online or onsite) targets beginner-level professionals seeking to master and deploy AI technologies in the semiconductor manufacturing domain.
By the conclusion of this program, attendees will be equipped to:
Understand fundamental AI principles and their practical application in semiconductor production.
Recognize specific manufacturing stages where AI integration is most effective.
Apply AI-driven tools to enhance output efficiency and maintain rigorous quality control.
Establish basic AI models to drive the optimization of manufacturing processes.
This live, instructor-led training, conducted in Bangkok (either online or on-site), targets intermediate data analysts aiming to master the use of RapidMiner for value estimation and projection, and for employing analytical tools in time series forecasting.
By the conclusion of this training, participants will be able to:
Implement the CRISP-DM methodology, select optimal machine learning algorithms, and refine model construction and performance outcomes.
Utilize RapidMiner to estimate and project values, and apply analytical tools for time series forecasting.
This live, instructor-led training in Bangkok (available online or onsite) provides an introductory overview of pattern recognition and machine learning. It explores practical applications within statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
By the end of this program, participants will be prepared to:
Apply core statistical methods to pattern recognition scenarios.
Utilize key models, including neural networks and kernel methods, for data analysis.
Implement advanced techniques to solve complex problems.
Enhance prediction accuracy by integrating various models.
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