Mašinsko učenje Training in Belgrade

This instructor-led, live training in Belgrade (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

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

• Understand the concept and benefits of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

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 tailored for intermediate-level AI developers, machine learning engineers, and system architects aiming to optimize AI models for edge deployment.

Upon completion of this training, participants will be capable of:

• Comprehending the obstacles and specifications associated with deploying AI models on edge devices.
• Employing model compression techniques to decrease the size and complexity of AI models.
• Leveraging quantization approaches to boost model efficiency on edge hardware.
• Executing pruning and other optimization strategies to elevate model performance.
• Deploying refined AI models across various edge devices.

This live, instructor-led training in Belgrade (conducted online or on-site) is designed for intermediate-level developers, data scientists, and tech enthusiasts aiming to develop practical skills in deploying AI models on edge devices for a variety of applications.

By the conclusion of this training, participants will be capable of:

• Understanding the fundamental principles of Edge AI and its key benefits.
• Setting up and configuring the edge computing infrastructure.
• Developing, training, and optimizing AI models for edge-specific deployment.
• Implementing functional AI solutions on edge devices.
• Evaluating and refining the performance of edge-deployed models.
• Managing ethical and security aspects in Edge AI applications.

This instructor-led, live training in Belgrade (online or onsite) targets advanced-level AI engineers and data scientists with intermediate-to-advanced experience who aim to enhance DeepSeek model performance, reduce latency, and deploy AI solutions efficiently using modern MLOps practices.

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

• Optimize DeepSeek models to improve efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models across both cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

Ova obuka koju vodi instruktur za Belgrade opremila napredne profesionalce veštinama za projektovanje, optimizaciju i primenu kompletnih TinyML procesa. Učesnici će naučiti da prikupljaju podatke, treniraju modele sa niskom potrošnjom i testiraju aplikacije u stvarnim uslovima koristeći praktične radionice.

Ova instruktorska, uživo obuka u Belgrade (onlajn ili na lokaciji) namenjena je programerima, naučnicima podataka i AI praktičarima srednjeg nivoa znanja koji žele da iskoriste TensorFlow Lite za Edge AI aplikacije.

Na kraju ove obuke, učesnici će moći da:

• Razumeju osnove TensorFlow Litea i njegovu ulogu u Edge AI-u.
• Razviju i optimizuju AI modele pomoću TensorFlow Litea.
• Implementiraju TensorFlow Lite modele na različite edge uređaje.
• Korišćenje alata i tehnika za konverziju i optimizaciju modela.
• Implementiraju praktične Edge AI aplikacije koristeći TensorFlow Lite.

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

Upon completion of this training, participants will be capable of:

• Designing and implementing AI models for autonomous decision-making.
• Developing control algorithms for autonomous navigation and obstacle avoidance.
• Ensuring safety and reliability in AI-powered autonomous systems.
• Integrating autonomous systems with existing robotics and AI frameworks.

Ova obuka sa trenerom uživo na temu Belgrade (online ili na lokaciji) namenjena je stručnjacima naprednog nivoa koji žele da prodube razumevanje računarske vizije i istraže mogućnosti Tenzora u razvoju sofisticiranih vizuelnih modela pomoću Google Colab.

Nakon završetka ove obuke, učesnici će moći da:

• Izgrade i obuku konvolucione neuronske mreže (CNN) koristeći TensorFlow.
• Izveste Google Colab za skalabilni i efikasan razvoj modela u oblaku.
• Implementiraju tehnike preobrade slika za zadatke iz računarske vizije.
• Implementiraju modele računarske vizije za primenu u stvarnom svetu.
• Koriste učenje prenosa (transfer learning) za poboljšavanje performansi CNN modela.
• Vizuelizuju i tumače rezultate modela za klasifikaciju slika.

This instructor-led course in Belgrade enables advanced professionals to fortify TinyML pipelines on edge devices. It covers the implementation of privacy-preserving strategies, hardening models against adversarial risks, and adhering to best practices for secure data management in constrained environments.

This instructor-led, live training in Belgrade (online or onsite) is designed for advanced professionals who want to deepen their knowledge of machine learning models, improve their hyperparameter tuning skills, and learn how to effectively deploy models using Google Colab.

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

• Implement advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
• Optimize model performance through hyperparameter tuning.
• Deploy machine learning models in real-world applications using Google Colab.
• Collaborate and manage large-scale machine learning projects in Google Colab.

This live, instructor-led training in Belgrade (available online or on-site) is aimed at intermediate-level professionals who want 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 Belgrade (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 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 session in Belgrade (online or onsite) is designed for advanced professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, enhancing efficiency, accuracy, and innovation in chip design and verification.

Upon completing this training, participants will be able to:

• Apply advanced AI techniques to optimize semiconductor design workflows.
• Incorporate machine learning models into EDA tools for improved design verification.
• Create AI-driven solutions to address complex design challenges in chip fabrication.
• Utilize neural networks to enhance the speed and accuracy of design automation.

Ovaj vodja-led trening uživo u Belgrade (online ili uživo u učionici) namenjen je naučnicima za podatke i razvojima srednjeg nivoa znanja koji žele da razumeju i primenjuju tehnike dubokog učenja koristeći okruženje Google Colab.

Na kraju ovog treninga, učesnici će moći da:

• podesite i navigirate Google Colab-om za projekte dubokog učenja.
• razumeju temelje neuralnih mreža.
• implementiraju modele dubokog učenja koristeći TensorFlow.
• obuku i procene modele dubokog učenja.
• iskoriste napredne funkcionalnosti TensorFlow-a za duboko učenje.

This instructor-led, live training in Belgrade (online or onsite) is designed for intermediate professionals who aim to understand and apply AI techniques for optimizing semiconductor fabrication processes.

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

• Comprehend AI methodologies used for process optimization in chip fabrication.
• Deploy AI models to improve yield and minimize defects.
• Analyze process data to pinpoint critical parameters for optimization.
• Utilize machine learning techniques to fine-tune semiconductor manufacturing processes.

This instructor-led, live training in Belgrade (online or onsite) is aimed at intermediate-level participants who wish 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:

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This instructor-led, live training in Belgrade (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.

Ova obuka uživo, koju vodi instruktor na lokaciji Belgrade, opremi napredne praktičare da optimizuju TinyML modele za ugrađene uređaje sa ograničenim resursima. Učesnici će primenjivati kvantizaciju i orezivanje, implementirati tokove zaključavanja sa niskom latencijom i ispitivati performanse u odnosu na ograničenja memorije i energije.

Delivered as an instructor-led, live training in Belgrade (online or onsite), this course is designed for advanced professionals who wish to explore cutting-edge 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 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.

Ovo treniranje sa instruktivom uživo u <lokalizaciji> (onlajn ili uživo) usmereno je na naučnike podataka i programere koji žele da koriste modele mašinskog učenja u ML.NET za automatsko izvođenje projekcija iz izvršene analize podataka za aplikacije u preduzeću.

Na kraju ovog treninga, učesnici će moći da:

• Instališete ML.NET i integrišete ga u razvojno okruženje aplikacija.
• Razumete principe mašinskog učenja koji stoji iza ML.NET alata i algoritama.
• Napravite i obučite modele mašinskog učenja kako bi pametno izvodili prognoze na osnovu datih podataka.
• Procenite performanse modela mašinskog učenja koristeći metrike ML.NET-a.
• Optimizujte tačnost postojećih modela mašinskog učenja na osnovu ML.NET okvira.
• Primene koncepte mašinskog učenja iz ML.NET-a na druge primene nauke o podacima.

This instructor-led, live training in Belgrade (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.

Upon completing this training, participants will be able to:

• Grasp 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 Belgrade (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision specialists aiming to broaden their knowledge and skills in applying deep learning to text-to-image generation.

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

• Analyze advanced deep learning architectures and techniques for text-to-image generation.
• Build complex models and apply optimizations for superior image synthesis.
• Improve performance and scalability for large datasets and complex models.
• Optimize hyperparameters to enhance model performance and generalization.
• Incorporate Stable Diffusion into other deep learning frameworks and tools

Ovaj trener vodio, uživo obuku u Belgrade (online ili uživo) namenjen je profesionalcima iz oblasti kiberbezbednosti srednjeg i naprednog nivoa koji žele da podignu svoje veštine u oblasti detekcije pretnji pokretanih veštačkom inteligencijom i upravljanja incidentima.

Na kraju ove obuke, učesnici će moći da:

• implementiraju napredne AI algoritme za detekciju pretnji u realnom vremenu.
• prilagode AI modele za specifične izazove u oblasti kiberbezbednosti.
• razviju automatizovane tokove rada za odgovor na pretnje.
• osiguraju bezbednosne alate pokretane veštačkom inteligencijom od adversarialnih napada.

This instructor-led, live training in Belgrade (online or on-site) is designed for intermediate-level embedded systems engineers and AI developers aiming 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 principles of TinyML and its advantages for edge AI applications.
• Configure a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Leverage 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 Belgrade (online or onsite) is designed for entry-level cybersecurity professionals eager to learn how to use AI to enhance their threat detection and response capabilities.

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

• Grasp the applications of AI within cybersecurity.
• Deploy AI algorithms for effective threat detection.
• Automate incident response using AI tools.
• Integrate AI into current cybersecurity infrastructure.

Ovo instruktorsko, uživo obučavanje u Belgrade (onlajn ili na lokaciji) namenjeno je biolozima koji žele da razumeju kako AlphaFold funkcioniše i da koriste AlphaFold modele kao smernice u svojim eksperimentalnim studijama.

Krajem ovog obučavanja, učesnici će moći da:

• Razumeju osnovna načela AlphaFold-a.
• Nauče kako AlphaFold funkcioniše.
• Nauče kako da interpretiraju predikcije i rezultate AlphaFold-a.

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 (online or onsite) is aimed at 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 how it works for image generation.
• 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 program offers a comprehensive journey, bridging robust Python engineering principles with advanced AI system design. Participants will cultivate disciplined coding habits, master statistical and deep learning techniques, and construct production-ready generative AI and agent-based solutions. The curriculum emphasizes reliability, evaluation, safety, and real-world deployment over mere experimentation.

Ova obuka sa instruktorem uživo <lok> (online ili uživo) pruža uvid u oblast prepoznavanja obrazaca i mašinskog učenja. Bavi se praktičnim primenama u statistici, računarskoj nauci, obradi signala, računarskom videu, rudarenju podataka i bioinformatici.

Krajev ove obuke, učesnici će moći da:

• Primenjuju ključne statističke metode u prepoznavanju obrazaca.
• Koriste ključne modele, kao što su neuronetske mreže i kernel metode, za analizu podataka.
• Primjenjuju napredne tehnike za rešavanje složenih problema.
• Poboljšaju tačnost predviđanja kombinovanjem različitih modela.

This instructor-led live training in Belgrade (online or on-site) is designed for data scientists who want to use 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 practical, instructor-led course serves as a logical next step for those who have completed the Python for Data Analysis course.

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

Emphasis is placed on practical application, utilizing familiar tools like Python, Pandas, and Jupyter Notebook, without the need for an advanced mathematical background.

Ova obuka uživo sa instruktora u Belgrade (online ili uživo na lokaciji) namenjena je developerima i naučnicima za podatke koji žele da grade, uspostave i upravljaju tokom rada mašinskog učenja na Kubernetes platformi.

Koncem ove obuke, učesnici će biti u stanju da:

• Instaliraju i konfigurisu Kubeflow u lokalnom okruženju (on-premise) i u oblaku.
• Grade, uspostave i upravljaju tokom rada ML zasnivaći se na Docker kontejnerima i Kubernetes platformi.
• Izvrše kompletne pipelajnove mašinskog učenja na različitim arhitekturama i облачним okruženjima.
• Iskoriste Kubeflow za pokretanje i upravljanje Jupyter notesbookovima.
• Grade opterećenja za obuku ML, podešavanje hiperparametara i serviranje preko više platformi.

This instructor-led, live training in Belgrade (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 Belgrade (online or onsite) is designed for intermediate-level data analysts, developers, and 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.

Ova obuka uživo sa trenerom u Belgrade (online ili na lokaciji) namenjena je programerima i naučnicima podataka koji žele da koriste TensorFlow 2.x za izgradnju prediktora, klasifikatora, generativnih modela, neuronskih mreža i slično.

Krajem ove obuke, učesnici će biti u mogućnosti da:

• Instaliraju i konfigurisuju TensorFlow 2.x.
• Razumeju prednosti TensorFlow 2.x u odnosu na prethodne verzije.
• Izgrade modele za duboko učenje.
• Implementiraju napredni klasifikator slika.
• Postave model za duboko učenje u oblaku, na mobilne uređaje i IoT uređaje.