Computer Vision with Python Training Course

This immersive, practical course delves into advanced Python techniques, engineering best practices, and widely adopted design patterns to help you build maintainable, testable, and high-performance Python applications. The curriculum emphasizes modern tooling, type hinting, concurrency models, architectural patterns, and deployment-ready workflows.

Delivered as an instructor-led live training (available online or onsite), this programme is tailored for intermediate to advanced Python developers who aim to adopt professional practices and patterns for production-grade Python systems.

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

• Leverage Python typing, dataclasses, and type-checking to enhance code reliability.
• Utilize design patterns and architectural principles to structure robust applications.
• Correctly implement concurrency and parallelism using asyncio and multiprocessing.
• Develop well-tested code using pytest, property-based testing, and CI pipelines.
• Profile, optimize, and harden Python applications for production environments.
• Package, distribute, and deploy Python projects using modern tools and containers.

Format of the Course

• Interactive lectures and concise demonstrations.
• Hands-on labs and coding exercises each day.
• A capstone mini-project integrating patterns, testing, and deployment.

Course Customization Options

• To request customized training or focus areas (data, web, or infrastructure), please contact us to arrange.

This course delivers practical engineering strategies for designing, constructing, evaluating, and deploying agentic (autonomous) systems using Python. Key topics include the agent loop, tool integration, memory and state management, orchestration patterns, safety controls, and essential production considerations.

Delivered as instructor-led live training (either online or onsite), this program targets intermediate to advanced ML engineers, AI developers, and software engineers who aim to build robust, production-ready autonomous agents using Python.

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

• Design and implement the core agent loop and decision-making workflows.
• Integrate external tools and APIs to expand agent capabilities.
• Implement short-term and long-term memory architectures for agents.
• Coordinate multi-step orchestrations and ensure agent composability.
• Apply best practices for safety, access control, and observability in deployed agents.

Course Format

• Interactive lectures and discussions.
• Hands-on labs focused on building agents with Python and popular SDKs.
• Project-based exercises resulting in deployable prototypes.

Customization Options

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

This course delves into practical methodologies for Data Science and AI leveraging Python, empowering professionals with the expertise to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. Key topics include the CRISP-DM workflow, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. It is particularly suitable for beginners looking to obtain a Python data science certification and receive career-focused analytics training.

Artificial Intelligence with Python involves building intelligent systems by leveraging Python’s comprehensive ecosystem of AI and machine learning libraries.

This instructor-led, live training (available online or onsite) is designed for intermediate-level Python programmers who want to design, implement, and deploy AI solutions using Python.

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

• Implement AI algorithms using Python’s core AI libraries.
• Work with supervised, unsupervised, and reinforcement learning models.
• Integrate AI solutions into existing applications and workflows.
• Evaluate model performance and optimize for accuracy and efficiency.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization solutions for real-time AI applications in computer vision and natural language processing, particularly on Huawei Ascend hardware.

This instructor-led live training, available online or onsite, is designed for intermediate-level AI professionals looking to develop, deploy, and optimize vision and language models using the CANN SDK for production environments.

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

• Deploy and optimize CV and NLP models utilizing CANN and AscendCL.
• Leverage CANN tools to convert models and seamlessly integrate them into active pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures combined with live demonstrations.
• Practical hands-on labs focusing on model deployment and performance profiling.
• Live pipeline design utilizing real-world CV and NLP use cases.

Customization Options

• To request a customized training session for this course, please get in touch with us to arrange details.

This instructor-led, live training in Greece (online or onsite) targets intermediate-level AI developers and computer vision engineers who aim to construct robust vision systems for autonomous driving applications.

Upon completing this training, participants will be able to:

• Grasp the fundamental concepts of computer vision within autonomous vehicles.
• Deploy algorithms for object detection, lane detection, and semantic segmentation.
• Integrate vision systems with other autonomous vehicle subsystems.
• Apply deep learning techniques for advanced perception tasks.
• Evaluate the performance of computer vision models in real-world scenarios.

This instructor-led live training in Greece (online or onsite) is designed for advanced professionals who wish to expand their expertise in computer vision and investigate TensorFlow's capabilities for developing advanced vision models using Google Colab.

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

• Construct and train Convolutional Neural Networks (CNNs) using TensorFlow.
• Utilize Google Colab for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques specifically for computer vision applications.
• Deploy computer vision models for practical, real-world solutions.
• Employ transfer learning to optimize the performance of CNN models.
• Visualize and interpret outcomes from image classification models.

This instructor-led, live training in Greece (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

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

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

This instructor-led, live training in Greece (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals seeking to implement and optimize computer vision models for real-time processing on edge hardware.

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

• Grasp the core concepts of Edge AI and its role in computer vision.
• Deploy optimized deep learning models on edge devices for real-time image and video analysis.
• Utilize frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Enhance AI models for performance, power efficiency, and low-latency inference.

This instructor-led, live training in Greece (online or onsite) is designed for beginner-level law enforcement officers aiming to transition from manual facial sketching to using AI tools for developing facial recognition systems.

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

• Grasp the core principles of Artificial Intelligence and Machine Learning.
• Acquire foundational knowledge of digital image processing and its role in facial recognition.
• Build proficiency in using AI tools and frameworks to construct facial recognition models.
• Gain practical experience in creating, training, and evaluating facial recognition systems.
• Comprehend the ethical implications and best practices associated with facial recognition technology.

This instructor-led live training (online or onsite) targets developers who wish to use the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

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

• Set up the necessary development environment that integrates FastAPI, React, and MongoDB.
• Understand the key concepts, features, and benefits of the FARM stack.
• Learn how to build REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (front end and back end) using the FARM stack.

This instructor-led, live training in Greece (online or onsite) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs easier and faster.

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

• Set up the necessary development environment to develop APIs with Python and FastAPI.
• Create APIs quicker and easier using the FastAPI library.
• Learn how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication in APIs using the FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led, live training in Greece (online or onsite) is designed for beginner to intermediate-level researchers and laboratory professionals who wish to process and analyze images of histological tissues, blood cells, algae, and other biological samples.

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

• Navigating the Fiji interface and applying ImageJ’s core functionalities.
• Preprocessing and enhancing scientific images to improve analysis quality.
• Performing quantitative image analysis, such as cell counting and area measurement.
• Automating repetitive tasks through the use of macros and plugins.
• Tailoring workflows to meet specific image analysis requirements in biological research.

This instructor-led, live training in Greece (online or onsite) is aimed at intermediate-level professionals who wish to use Vision Builder AI to design, implement, and optimize automated inspection systems for SMT (Surface-Mount Technology) processes.

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

• Set up and configure automated inspections using Vision Builder AI.
• Acquire and preprocess high-quality images for analysis.
• Implement logic-based decisions for defect detection and process validation.
• Generate inspection reports and optimize system performance.

This guided, live training session in Greece (online or in person) is designed for developers, researchers, and data scientists at intermediate to advanced levels who aim to master the implementation of real-time object detection using YOLOv7.

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

• Grasp the core principles underlying object detection.
• Successfully install and configure YOLOv7 for various detection tasks.
• Train and evaluate custom object detection models utilizing YOLOv7.
• Seamlessly integrate YOLOv7 with existing computer vision frameworks and tools.
• Address and resolve common challenges associated with YOLOv7 implementation.