Supervised Learning Training in Belgrade

This instructor-led, live training in Belgrade (online or onsite) targets 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 Belgrade (online or onsite) is designed for intermediate-level professionals who aim to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to improve production efficiency and reduce unexpected equipment failures.

By the end of this training, participants will be able to:

• Deploy AI models to predict equipment failures in semiconductor production.
• Analyze maintenance data to uncover patterns and trends that signal potential issues.
• Incorporate AI-driven predictive maintenance into current manufacturing workflows.
• Lower downtime and maintenance expenses via proactive equipment management.

This instructor-led, live training in Belgrade (online or onsite) is designed for 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 their application in 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 guided, live training session (available online or on-site) targets technical professionals with a background in machine learning who aim to enhance model performance for identifying complex patterns within big data using AutoML frameworks.

This instructor-led, live training in Belgrade (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 Belgrade (online or onsite) is designed for data scientists and less technical individuals who wish to utilize Auto-Keras to automate the selection and optimization of machine learning models.

By the end of this training, participants will be able to:

• Automate the process of training highly efficient machine learning models.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning models.
• Use the power of machine learning to solve real-world business problems.

This instructor-led, live training in Belgrade (online or onsite) offers 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.

This instructor-led live training in Belgrade (online or onsite) is designed for data scientists and analysts who wish to automate, evaluate, and manage predictive models using DataRobot's machine learning capabilities.

By the end of this training, participants will be able to:

• Load datasets in DataRobot to analyze, assess, and quality check data.
• Build and train models to identify important variables and meet prediction targets.
• Interpret models to create valuable insights that are useful in making business decisions.
• Monitor and manage models to maintain an optimized prediction performance.

This instructor-led, live training in Belgrade (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 session (available online or onsite) is designed for developers aiming to utilize Google’s ML Kit to construct machine learning models optimized for mobile device processing.

Upon completing this training, participants will be able to:

• Establish the necessary development environment to commence the creation of machine learning features for mobile applications.
• Integrate new machine learning capabilities into Android and iOS apps using the ML Kit APIs. 
• Refine and optimize existing applications by leveraging the ML Kit SDK for on-device processing and deployment.

Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.

Format of the Course

• This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.

RapidMiner is an open-source data science software platform designed for rapid application prototyping and development. It features an integrated environment supporting data preparation, machine learning, deep learning, text mining, and predictive analytics.

During this instructor-led live training, participants will learn to utilize RapidMiner Studio for data preparation, machine learning, and the deployment of predictive models.

Upon completing this training, participants will be able to:

• Install and configure RapidMiner
• Prepare and visualize data using RapidMiner
• Validate machine learning models
• Integrate data sources and create predictive models
• Implement predictive analytics within business processes
• Troubleshoot and optimize RapidMiner

Audience

• Data scientists
• Engineers
• Developers

Format of the Course

• A mix of lectures, discussions, exercises, and extensive hands-on practice

Note

• To request customized training for this course, please contact us to arrange.