Online or onsite, instructor-led live Supervised Learning training courses demonstrate through interactive hands-on practice how to use supervised machine learning techniques to train models, make predictions, and analyze data patterns effectively.
Supervised Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Supervised Learning training can be carried out locally on customer premises in Berlin or in NobleProg corporate training centers in Berlin.
Supervised Learning is also known as Supervised Machine Learning.
Our training facilities are located at Brückenstr. 4 in Berlin. Located on the fourth floor of a well-kept office building, our premises offer enough space for successful training courses in the heart of Berlin, within walking distance of the Jannowitzbrücke station.
Directions
The NobleProg training facilities are located in the heart of Berlin's Mitte district, just one underground station from Alexanderplatz, one of the centres of this vibrant city. By public transport you can reach us either by underground line U8 to Jannowitzbrücke station, followed by about 100 meters on foot.
Parking
Cars can be parked in the area along Brückenstr. and the nearby side streets, even if you may have to search for a moment. There is no charge for parking.
Local Amenities
Around the Rosenthaler Platz there are numerous small restaurants and shops where you can eat well and cheaply. There are also some hotels close by if you need accommodation for the training.
Our training facilities are located at Dianastrasse 46 in Potsdam-Babelsberg.
Our spacious training rooms are located directly opposite the Filmstudios Babelsberg and offer optimal training conditions for your needs.
Arrival
The NobleProg training facilities are conveniently located near the Medienstadt Babelsberg railway station,
and the A115 motorway is also easily accessible.
Parking
Parking is available in the surrounding streets around our training rooms.
Local Services
Potsdam offers numerous hotels and restaurants and is easily accessible thanks to its well-developed public transport system.
This instructor-led, live training in Berlin (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:
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 optimize chatbot performance over time.
This instructor-led, live training in Berlin (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 in Berlin (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 Berlin (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 Berlin (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.
This instructor-led, live training in Berlin (online or onsite) is aimed at data scientists, data analysts, and developers who wish to explore AutoML products and features to create and deploy custom ML training models with minimal effort.
By the end of this training, participants will be able to:
Explore the AutoML product line to implement different services for various data types.
Prepare and label datasets to create custom ML models.
Train and manage models to produce accurate and fair machine learning models.
Make predictions using trained models to meet business objectives and needs.
This instructor-led, live training in (online or onsite) is aimed at developers who wish to use Google’s ML Kit to build machine learning models that are optimized for processing on mobile devices.
By the end of this training, participants will be able to:
Set up the necessary development environment to start developing machine learning features for mobile apps.
Integrate new machine learning technologies into Android and iOS apps using the ML Kit APIs.
Enhance and optimize existing apps using the ML Kit SDK for on-device processing and deployment.
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