Machine Learning Training in Prague

This instructor-led, live training in Prague (online or on-site) is designed for beginner-level professionals who aim to grasp the concept of pre-trained models and learn how to apply them to solve real-world problems without the need to build models from the ground up.

Upon completion of this training, participants will be able to:

• Comprehend the concept and advantages of pre-trained models.
• Examine various pre-trained model architectures and their specific use cases.
• Fine-tune a pre-trained model for designated tasks.
• Implement pre-trained models in straightforward machine learning projects.

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) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.

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

• Understand the challenges and requirements of deploying AI models on edge devices.
• Apply model compression techniques to reduce the size and complexity of AI models.
• Utilize quantization methods to enhance model efficiency on edge hardware.
• Implement pruning and other optimization techniques to improve model performance.
• Deploy optimized AI models on various edge devices.

This live, instructor-led training in Prague (online or onsite) is tailored for intermediate-level developers, data scientists, and tech enthusiasts seeking to master the practical skills required for deploying AI models on edge devices across diverse applications.

By the end of this program, participants will be capable of:

• Understanding the fundamentals and benefits of Edge AI.
• Configuring the necessary edge computing environment.
• Developing and optimizing AI models for edge deployment.
• Implementing functional AI solutions on edge hardware.
• Evaluating and refining the performance of edge-deployed models.
• Addressing ethical and security aspects in Edge AI contexts.

This instructor-led, live training in Prague (online or onsite) is designed for advanced AI engineers and data scientists with intermediate-to-advanced expertise who aim to improve DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using contemporary MLOps practices.

Upon completion of this training, participants will be able to:

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Apply best practices for MLOps and model versioning.
• Deploy DeepSeek models on both cloud and on-premise infrastructure.
• Effectively monitor, maintain, and scale AI solutions.

This instructor-guided training in Prague empowers senior professionals with the capabilities to architect, optimize, and release complete TinyML workflows. Learners will master data acquisition, low-power model training, and real-world application testing through hands-on laboratory exercises.

This instructor-led, live training in Prague (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Prague (online or onsite) is designed for advanced-level professionals seeking to master the technologies underpinning autonomous systems.

Upon completion of this training, participants will be able to:

• Design and deploy AI models for autonomous decision-making.
• Create control algorithms for autonomous navigation and obstacle avoidance.
• Ensure safety and reliability within AI-driven autonomous systems.
• Integrate autonomous systems with existing robotics and AI frameworks.

This instructor-led, live training on Prague (available online or onsite) is designed for advanced professionals aiming to deepen their mastery of computer vision and harness TensorFlow’s power to develop sophisticated vision models via Google Colab.

By the conclusion of this training, participants will possess the ability to:

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Utilize Google Colab to facilitate scalable and efficient cloud-based model development.
• Apply image preprocessing techniques specifically for computer vision tasks.
• Deploy computer vision models into practical, real-world scenarios.
• Enhance the performance of CNN models by implementing transfer learning.
• Visualize and interpret the outcomes of image classification models.

This guided training session on Prague empowers advanced professionals to secure TinyML pipelines on edge hardware. Learners will acquire the skills to apply privacy-preserving methods, reinforce models against adversarial threats, and adopt best practices for safe data processing in resource-limited contexts.

This instructor-led, live training in Prague (online or onsite) targets advanced professionals aiming to deepen their understanding of machine learning models, refine their hyperparameter tuning skills, and master effective model deployment via Google Colab.

Upon completion of this training, participants will be able to:

• Build advanced machine learning models using widely adopted frameworks such as Scikit-learn and TensorFlow.
• Enhance model performance through systematic hyperparameter tuning.
• Deploy machine learning models into real-world applications utilizing Google Colab.
• Collaborate on and manage large-scale machine learning projects within Google Colab.

This instructor-led live training in Prague (online or onsite) is aimed at intermediate-level professionals who wish to apply AI techniques to optimize yield management in semiconductor manufacturing.

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

• Analyze production data to identify factors affecting yield rates.
• Implement AI algorithms to enhance yield management processes.
• Optimize production parameters to reduce defects and improve yields.
• Integrate AI-driven yield management into existing production workflows.

This instructor-led, live training in Prague (online or onsite) is aimed at intermediate-level business and AI professionals who wish to apply machine learning in business, forecasting, and AI-driven systems using real case studies and Python-based tools.

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

• Understand how machine learning fits within AI and business strategy.
• Apply supervised and unsupervised learning techniques to structured business problems.
• Preprocess and transform data for modeling.
• Use neural networks for classification and prediction tasks.
• Perform sales forecasting using statistical and ML-based methods.
• Implement clustering and association rule mining for customer segmentation and pattern discovery.

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 advanced professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, improving efficiency, accuracy, and innovation in chip design and verification.

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

• Apply advanced AI techniques to optimize semiconductor design processes.
• Integrate machine learning models into EDA tools for enhanced design verification.
• Develop AI-driven solutions for complex design challenges in chip fabrication.
• Leverage neural networks for improving the accuracy and speed of design automation.

This instructor-led, live training in Prague (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

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

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This guided, live training in Prague (online or onsite) is designed for intermediate-level professionals who want to understand and apply AI techniques for optimizing semiconductor fabrication processes.

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

• Understand AI methodologies for process optimization in chip fabrication.
• Implement AI models to enhance yield and reduce defects.
• Analyze process data to identify key parameters for optimization.
• Apply machine learning techniques to fine-tune semiconductor manufacturing processes.

This instructor-led, live training in Prague (online or onsite) is designed for intermediate-level participants seeking to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

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

• Configure Apache Airflow for orchestrating machine learning workflows.
• Automate tasks related to data preprocessing, model training, and validation.
• Integrate Airflow with various machine learning frameworks and tools.
• Deploy machine learning models through automated pipelines.
• Monitor and optimize machine learning workflows in a production environment.

This instructor-led, live training in Prague (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment.

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

• Set up and navigate Google Colab for machine learning projects.
• Understand and apply various machine learning algorithms.
• Use libraries like Scikit-learn to analyze and predict data.
• Implement supervised and unsupervised learning models.
• Optimize and evaluate machine learning models effectively.

This live, instructor-led training in Prague prepares advanced practitioners to optimize TinyML models for resource-constrained embedded devices. Participants will learn to apply quantization and pruning, build low-latency inference pipelines, and evaluate performance against memory and energy limits.

Delivered as an instructor-led, live training in Prague (online or onsite), this program is designed for advanced professionals interested in exploring state-of-the-art XAI techniques for deep learning models, focusing on the development of interpretable AI systems.

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

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.

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 on-site) is designed for data scientists and developers who wish to utilize ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.

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

• Install ML.NET and integrate it into the application development environment.
• Understand the machine learning principles behind ML.NET tools and algorithms.
• Build and train machine learning models to perform predictions with the provided data smartly.
• Evaluate the performance of a machine learning model using the ML.NET metrics.
• Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
• Apply the machine learning concepts of ML.NET to other data science applications.

This instructor-led, live training in Prague (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling using neural networks.

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

• Understand the core concepts and types of machine learning.
• Apply key algorithms for classification, regression, clustering, and association analysis.
• Perform exploratory data analysis and data preparation using Python.
• Use neural networks for nonlinear modeling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimize model performance using visual and statistical techniques.

This instructor-led, live training in Prague (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts looking to expand their knowledge and skills in deep learning for text-to-image generation.

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

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in Prague (online or on-site) is designed for intermediate to advanced cybersecurity professionals who wish to enhance their skills in AI-driven threat detection and incident response.

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

• Implement advanced AI algorithms for real-time threat detection.
• Customize AI models for specific cybersecurity challenges.
• Develop automation workflows for threat response.
• Secure AI-driven security tools against adversarial attacks.

This instructor-led, live training in Prague (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who want to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be able to:

• Grasp the core concepts of TinyML and understand its advantages for edge AI applications.
• Establish a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Optimize AI models to meet power efficiency and memory limitations.

This instructor-led, live training in Prague (online or in-person) is designed for entry-level cybersecurity professionals who wish to learn how to leverage AI for improved threat detection and response capabilities.

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

• Understand AI applications in cybersecurity.
• Implement AI algorithms for threat detection.
• Automate incident response with AI tools.
• Integrate AI into existing cybersecurity infrastructure.

This instructor-led, live training in Prague (online or onsite) is designed for biologists who want to understand how AlphaFold works and use AlphaFold models to guide their experimental studies.

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

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

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 (online or onsite) is designed for data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

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

• Understand the principles of Stable Diffusion and its image generation workflow.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

This 8-day course takes participants on a comprehensive journey from robust Python engineering basics to advanced AI system architecture. Attendees will cultivate disciplined coding habits, gain mastery over statistical and deep learning techniques, and construct production-ready generative AI and agent-based solutions. The emphasis is placed on reliability, evaluation, safety, and real-world deployment, moving beyond mere experimentation.

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.

This instructor-led live training in Prague (online or on-site) targets data scientists who wish to utilize TensorFlow to analyze potential fraud data.

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

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

This hands-on, instructor-led training serves as a logical next step following the Python for Data Analysis course.

It introduces participants to the fundamental principles of Machine Learning and demonstrates how to apply them directly to data analysis tasks, including prediction, classification, and segmentation.

The curriculum emphasizes practical understanding of Machine Learning, utilizing familiar tools such as Python, Pandas, and Jupyter Notebook, without requiring an advanced mathematical background.

This live, instructor-led training in Prague—delivered online or on-site—is designed for developers and data scientists looking to construct, deploy, and manage machine learning workflows on Kubernetes.

By the end of the session, participants will be equipped to:

• Install and configure Kubeflow in on-premises and cloud environments.
• Create, deploy, and manage ML workflows using Docker containers and Kubernetes.
• Run complete machine learning pipelines across diverse architectures and cloud platforms.
• Use Kubeflow to launch and control Jupyter notebooks.
• Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Prague (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.

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

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
• Prepare, validate and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.

This instructor-led, live training in Prague (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

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

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.

This instructor-led live training in Prague (available online or on-site) is designed for developers and data scientists who aim to utilize TensorFlow 2.x for building predictors, classifiers, generative models, and neural networks.

By the end of this training, participants will have the capability to:

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous iterations.
• Build deep learning models.
• Implement advanced image classifiers.
• Deploy deep learning models to cloud, mobile, and IoT platforms.