Available either online or on-site, these instructor-led Machine Learning (ML) live training courses utilize hands-on practice to demonstrate the application of machine learning techniques and tools for resolving real-world problems across various sectors. NobleProg’s ML curriculum encompasses a range of programming languages and frameworks, including Python, R language, and Matlab. The courses address specific industry applications, particularly within Finance, Banking, and Insurance, covering both the core fundamentals of Machine Learning and more advanced methodologies such as Deep Learning.
Machine Learning training can be accessed as either "online live training" or "onsite live training". Online live training (also referred to as "remote live training") is conducted through an interactive remote desktop. Onsite live training can be delivered directly at the client's premises in Sofia or within NobleProg’s corporate training centers in Sofia.
NobleProg – Your Local Training Provider
Crystal Business Center
ул. "Осогово" 40, Sofia, Bulgaria, 1303
Crystal Business Center is located in the central part of Sofia, on the corner of "Osogovo" street. and "Todor Aleksandrov" blvd. The building is easily accessible by metro (only 50 m from Opalchenska station) and other public transport. Its total area is 8000 sq.m. The office area is 6171 sq.m.
This instructor-led, live training in Sofia (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 Sofia (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 Sofia (online or onsite) is tailored for intermediate-level AI developers, machine learning engineers, and system architects who seek to optimize AI models for edge deployment.
Upon completion of this training, participants will be able to:
Comprehend the challenges and requirements associated with deploying AI models on edge devices.
Apply model compression techniques to decrease the size and complexity of AI models.
Leverage quantization methods to boost model efficiency on edge hardware.
Implement pruning and additional optimization techniques to enhance model performance.
Deploy optimized AI models across various edge devices.
This live, instructor-led training in Sofia (delivered online or on-site) is designed for intermediate developers, data scientists, and tech enthusiasts looking to acquire hands-on expertise in deploying AI models on edge devices for a variety of applications.
Upon completion of this training, participants will be capable of:
Understanding the fundamental principles of Edge AI and its key benefits.
Setting up and configuring an edge computing environment.
Developing, training, and optimizing AI models for edge deployment.
Implementing practical AI solutions on edge hardware.
Evaluating and enhancing the performance of edge-deployed models.
Addressing ethical and security implications in Edge AI applications.
This instructor-led, live training in Sofia (online or onsite) is aimed at advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices.
By the end of this training, participants will be able to:
Optimize DeepSeek models for efficiency, accuracy, and scalability.
Implement best practices for MLOps and model versioning.
Deploy DeepSeek models on cloud and on-premise infrastructure.
Monitor, maintain, and scale AI solutions effectively.
This live training in Sofia empowers intermediate practitioners to build automated MLOps pipelines on Kubernetes. Participants will design CI/CD workflows, implement GitOps strategies, and deploy ML models using containerized infrastructure to achieve scalable and reproducible machine learning operations.
This hands-on training in Sofia provides the skills needed to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, create scalable pipelines, and manage production-ready workloads following best practices.
This instructor-led program in Sofia provides advanced professionals with the expertise to design, refine, and implement complete TinyML workflows. Through practical labs, learners will master data collection, the training of low-power models, and the validation of real-world applications.
This instructor-led, live training in Sofia (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 Sofia (online or onsite) is designed for advanced professionals seeking to master the technologies underlying autonomous systems.
Upon completion of this training, participants will be able to:
Design and implement AI models for autonomous decision-making.
Develop control algorithms for autonomous navigation and obstacle avoidance.
Ensure safety and reliability in AI-powered autonomous systems.
Integrate autonomous systems with existing robotics and AI frameworks.
This live, instructor-led training in Sofia (online or onsite) is aimed at experienced professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.
By the end of this training, participants will be able to:
Build and train convolutional neural networks (CNNs) using TensorFlow.
Leverage Google Colab for scalable and efficient cloud-based model development.
Implement image preprocessing techniques for computer vision tasks.
Deploy computer vision models for real-world applications.
Use transfer learning to enhance the performance of CNN models.
Visualize and interpret the results of image classification models.
This instructor-led training on Sofia empowers advanced professionals to secure TinyML pipelines on edge devices. You will learn to implement privacy-preserving techniques, reinforce models against adversarial threats, and apply best practices for secure data handling in constrained environments.
This instructor-led live training in Sofia (online or onsite) is designed for advanced professionals who wish 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 instructor-led live training in Sofia (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 Sofia (online or onsite) targets 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.
Upon completion of this training, participants will be able to:
Grasp how machine learning integrates with AI and business strategy.
Apply supervised and unsupervised learning techniques to solve structured business problems.
Preprocess and transform data for modeling purposes.
Utilize neural networks for classification and prediction tasks.
Conduct sales forecasting using both statistical and ML-based methods.
Implement clustering and association rule mining for customer segmentation and pattern discovery.
This instructor-led, live training in Sofia (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 Sofia (online or onsite) targets advanced professionals seeking to apply state-of-the-art AI techniques to semiconductor design automation, thereby enhancing efficiency, accuracy, and innovation in chip design and verification.
Upon completion of this training, participants will be equipped to:
Utilize advanced AI techniques to optimize semiconductor design workflows.
Integrate machine learning models into EDA tools to improve design verification.
Create AI-powered solutions for complex design challenges in chip fabrication.
Harness neural networks to boost the speed and accuracy of design automation.
This instructor-led, live training in Sofia (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 instructor-led, live training in Sofia (online or on-site) is designed for intermediate-level professionals seeking to understand and apply AI techniques for optimizing semiconductor fabrication processes.
By the conclusion of this training, participants will be capable of:
Understanding AI methodologies for process optimization in chip fabrication.
Implementing AI models to enhance yield and reduce defects.
Analyzing process data to identify key parameters for optimization.
Applying machine learning techniques to fine-tune semiconductor manufacturing processes.
This instructor-led live training, delivered Sofia (online or onsite), is designed for intermediate-level participants who wish to automate and manage machine learning workflows. The curriculum covers model training, validation, and deployment using Apache Airflow.
Upon completion of this training, participants will be equipped to:
Configure Apache Airflow specifically for orchestrating machine learning workflows.
Automate essential tasks such as data preprocessing, model training, and validation.
Seamlessly integrate Airflow with various machine learning frameworks and tools.
Deploy machine learning models through the use of automated pipelines.
Monitor and optimize machine learning workflows within production environments.
This instructor-led, live training in Sofia (online or onsite) is designed for intermediate-level data scientists and developers who want to efficiently apply machine learning algorithms 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 session in Sofia empowers advanced practitioners to optimize TinyML models for embedded devices operating under strict resource constraints. Learners will apply quantization and pruning techniques, build low-latency inference pipelines, and evaluate performance against memory and energy boundaries.
This instructor-led, live training in Sofia (online or onsite) is designed for advanced professionals who wish to explore state-of-the-art XAI techniques for deep learning models, focusing on the development of interpretable AI systems.
Upon completion of this training, participants will be able to:
Grasp the challenges associated with explainability in deep learning.
Apply advanced XAI techniques to neural networks.
Interpret the decisions generated by deep learning models.
Assess the balance between model performance and transparency.
This instructor-led, live training in Sofia (online or onsite) is designed for professional beginners who want to comprehend and apply AI technologies within the semiconductor manufacturing industry.
Upon completing this training, participants will be capable of:
Grasping the fundamental principles of AI and their application in semiconductor manufacturing.
Pinpointing specific areas in semiconductor manufacturing where AI can be effectively utilized.
Employing AI tools and techniques to improve production efficiency and quality assurance.
Deploying basic AI models to streamline manufacturing operations.
This instructor-led training in Sofia supports technical professionals in containerizing complete ML pipelines using Docker. Learners will gain proficiency in creating reproducible environments, orchestrating training and inference workloads, and establishing CI/CD processes for scalable MLOps deployments.
This instructor-led live training in Sofia (online or onsite) is designed for data scientists and developers who intend to use ML.NET machine learning models to automatically derive projections from 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 Sofia (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:
Grasp the fundamental concepts and categories of machine learning.
Utilize essential algorithms for classification, regression, clustering, and association analysis.
Conduct exploratory data analysis and prepare data using Python.
Leverage neural networks for nonlinear modeling tasks.
Deploy predictive analytics for business forecasting, including sales data.
Assess and enhance model performance through visual and statistical techniques.
This instructor-led, live training in Sofia (available online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision professionals seeking to expand their knowledge and skills in deep learning for text-to-image generation.
By the conclusion of this training, participants will be able to:
Understand advanced deep learning architectures and techniques specific to text-to-image generation.
Implement sophisticated models and optimizations aimed at high-quality image synthesis.
Optimize performance and scalability for processing large datasets and complex models.
Tune hyperparameters to achieve superior model performance and generalization.
Integrate Stable Diffusion with other deep learning frameworks and tools.
This instructor-led, live training in Sofia (online or onsite) is aimed at intermediate-level to advanced-level cybersecurity professionals who wish to elevate 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 Sofia (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers looking to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.
Upon completion of this training, participants will be able to:
Grasp the fundamentals 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.
Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
Enhance AI models for better power efficiency and memory utilization.
This instructor-led, live training in Sofia (online or onsite) is designed for beginner-level cybersecurity professionals eager to learn how to utilize AI for enhanced threat detection and response capabilities.
Upon completion of this training, participants will be able to:
Grasp AI applications within cybersecurity.
Deploy AI algorithms for threat identification.
Automate incident response using AI tools.
Incorporate AI into current cybersecurity infrastructure.
This instructor-led, live training in Sofia (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.
By the end of this training, participants will be able to:
Grasp the fundamental principles of AlphaFold.
Learn how AlphaFold operates.
Master the interpretation of AlphaFold predictions and results.
This instructor-led, live training in Sofia (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, available 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 functions 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.
Build hands-on proficiency in implementing Machine Learning methods with Python during this Sofia training. The course delves into core algorithms such as regression, classification, and clustering, guiding you through making sound modeling decisions, analyzing outputs, and verifying results via practical, real-world scenarios.
This hands-on training in Sofia enables developers to construct AI models using Python from first principles. You will gain proficiency in supervised learning, neural networks, and unsupervised methods by leveraging scikit-learn and Apache Spark. The course emphasizes practical Jupyter exercises designed for real-world problem-solving.
This instructor-led training in Sofia delves into the theoretical basis and practical application of Deep Reinforcement Learning with Python. Participants will construct and train DRL agents using TensorFlow or PyTorch, leveraging key algorithms like DQN and PPO to address complex real-world challenges.
A foundational training module in Sofia that covers AI fundamentals, from intelligent agents to machine learning. It prepares executives and architects to evaluate emerging AI trends, incorporate practical solutions, and enhance business agility through automated strategies.
Discover how Machine Learning and Deep Learning are reshaping the automotive landscape. This Sofia course explores fundamental concepts ranging from simple automation to autonomous decision-making, featuring neural networks and practical TensorFlow examples tailored for real-world applications.
This 8-day programme takes participants on a comprehensive journey, starting with robust Python engineering principles and advancing to sophisticated AI system design. Attendees will cultivate disciplined coding habits, gain expertise in statistical and deep learning techniques, and construct generative AI and agent-based systems ready for production environments. The curriculum prioritizes reliability, evaluation, safety, and real-world deployment over mere experimentation.
This three-day intensive on Sofia integrates the theoretical underpinnings with practical applications of Artificial Neural Networks, Machine Learning, and Deep Learning. Attendees will examine various network architectures, learning algorithms, and the associated mathematical foundations, progressing from foundational perceptrons to sophisticated deep learning methodologies.
Gain proficiency in Machine Learning algorithms, including Naive Bayes, Decision Trees, Neural Networks, SVMs, and Clustering, through this practical Sofia course. Develop skills in model assessment, bias-variance trade-offs, and deep learning to engineer robust predictive solutions.
This instructor-led, live training in Sofia (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.
Upon completion of this training, participants will be able to:
Apply fundamental statistical methods to pattern recognition.
Utilize essential models such as neural networks and kernel methods for data analysis.
Implement advanced techniques to solve complex problems.
Enhance prediction accuracy by integrating various models.
This instructor-led live training in Sofia (online or on-site) targets data scientists aiming to utilize TensorFlow for analyzing potential fraud data.
By the conclusion of this training, participants will be able to:
Build a fraud detection model using Python and TensorFlow.
Implement linear regressions and models to predict fraud.
Develop a complete AI application for fraud data analysis.
This instructor-led live training on Sofia delves into the fundamentals of AI, machine learning, and deep learning. Participants will leverage Python, Keras, and TensorFlow to develop practical telecom models, such as credit risk and churn prediction systems, acquiring hands-on skills for real-world data science applications.
This hands-on, instructor-led training serves as a logical next step after completing the Python for Data Analysis course.
It introduces key Machine Learning concepts and demonstrates their direct application to data analysis tasks, including prediction, classification, and segmentation.
The curriculum emphasizes practical understanding of Machine Learning using familiar tools like Python, Pandas, and Jupyter Notebook, without requiring an advanced mathematical background.
This live, instructor-led training in Sofia, offered online or on-site, is designed for developers and data scientists seeking to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this session, participants will gain the capability to:
Install and configure Kubeflow in on-premises and cloud settings.
Develop, deploy, and control ML workflows leveraging Docker containers and Kubernetes.
Operate full machine learning pipelines across diverse architectures and cloud environments.
Utilize Kubeflow to create and manage Jupyter notebooks.
Establish ML training, hyperparameter tuning, and serving workloads across various platforms.
This instructor-led live training, conducted in Sofia (online or onsite), is designed for engineers who wish to evaluate current approaches and tools. It aims to support intelligent decision-making on how to proceed with adopting MLOps in their organizations.
By the end of this training, participants will be able to:
Install and configure various MLOps frameworks and tools.
Assemble a team with the appropriate skills to construct and support 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 Sofia (online or onsite) is designed for intermediate-level data analysts, developers, or aspiring data scientists who aim to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.
Upon completion of this course, participants will be able to:
Comprehend and distinguish between key machine learning paradigms.
Explore data preprocessing techniques and model evaluation metrics.
Apply machine learning algorithms to address real-world data challenges.
Utilize Python libraries and Jupyter notebooks for practical development.
Construct models for prediction, classification, recommendation, and clustering.
This instructor-led, live training course in Sofia, delivered either online or on-site, is designed for developers and data scientists aiming to utilize TensorFlow 2.x for building predictors, classifiers, generative models, neural networks, and other complex systems.
By the end of this training, participants will be equipped to:
Install and configure the TensorFlow 2.x environment.
Understand the key benefits of TensorFlow 2.x over previous versions.
This 35-hour course in Sofia delves into the fundamentals of deep neural networks, covering CNNs, RNNs, and generative models such as GANs. Participants will gain practical experience with Theano and TensorFlow, acquiring the skills to build, train, and deploy production-grade deep learning models for real-world scenarios.
I thoroughly enjoyed the training and appreciated the deeper dive into the subject of Machine Learning. I appreciated the balance between theory and practical applications, especially the hands-on coding sessions. The trainer provided engaging examples and well-designed exercises that enhanced the learning experience. The course covered a wide range of topics, and Abhi demonstrated excellent expertise by answering all questions with clarity and ease.
Valentina
Course - Machine Learning
The training provided an interesting overview of deep learning models and related methods. The topic was quite new to me, but now I feel like I actually have an idea of what AI and ML can involve, what these terms consist of and how they can be used advantageously. In general, I liked the approach of starting with the statistical background and the basic learning models, such as linear regression, especially emphasizing the exercises in between.
Konstantin - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
Interesting knowledge
Gabriel - MINDEF
Course - Machine Learning with Python – 4 Days
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
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