Computer Vision with Python Training Course

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

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced Python developers looking to implement 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 containerization.

Course Format

• Interactive lectures accompanied by brief demonstrations.
• Hands-on labs and coding exercises conducted daily.
• A capstone mini-project that integrates patterns, testing, and deployment strategies.

Course Customization Options

• To request a customized training session or focus on specific areas (such as data, web, or infrastructure), please contact us to arrange.

This course imparts practical engineering methodologies for designing, developing, testing, and deploying agentic (autonomous) systems via Python. Key topics include the agent loop, tool integrations, memory and state management, orchestration patterns, safety mechanisms, and production-grade considerations.

Designed as an instructor-led live training session (available online or on-site), this program targets intermediate to advanced ML engineers, AI developers, and software engineers seeking to create robust, production-ready autonomous agents using Python.

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

• Designing and implementing the agent loop and decision-making workflows.
• Integrating external tools and APIs to expand agent functionality.
• Implementing memory architectures that support both short-term and long-term retention.
• Coordinating complex orchestrations and enabling agent composability.
• Applying 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.

Course Customization Options

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

This course offers a hands-on exploration of Data Science and AI methodologies using Python. It is designed to empower professionals with the essential skills to analyze data, develop machine learning models, and implement AI-powered solutions within business environments. The curriculum spans the CRISP-DM framework, statistical analysis, supervised and unsupervised learning techniques, deep learning with TensorFlow, natural language processing, big data processing with Spark, and the art of data-driven storytelling. It is particularly well-suited for beginners aiming to obtain a Python data science certification and acquire career-ready analytics training.

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

Designed for intermediate-level Python developers, this instructor-led live training (available online or onsite) focuses on designing, implementing, and deploying AI solutions using Python.

Upon completing this training, participants will be equipped 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 tools designed for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

This instructor-led, live training (available online or onsite) is tailored for intermediate-level AI professionals looking to build, 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 using CANN and AscendCL.
• Utilize CANN tools to convert models and integrate them into live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures and demonstrations.
• Hands-on labs focused on model deployment and performance profiling.
• Live pipeline design using real-world CV and NLP use cases.

Course Customization Options

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

This live, instructor-led session in Slovakia (online or onsite) targets intermediate-level AI developers and computer vision engineers aiming to build robust vision systems for autonomous driving applications.

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

• Understand the fundamental concepts of computer vision in autonomous vehicles.
• Implement 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 Slovakia (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

Upon completing this training, participants will be able to:

• Construct and train convolutional neural networks (CNNs) utilizing TensorFlow.
• Utilize Google Colab for scalable and efficient cloud-based model development.
• Apply image preprocessing techniques suitable for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Employ transfer learning to boost the performance of CNN models.
• Visualize and interpret the outcomes of image classification models.

This instructor-led, live training in Slovakia (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 Slovakia (online or onsite) is aimed at intermediate-level to advanced-level computer vision engineers, AI developers, and IoT professionals who wish to implement and optimize computer vision models for real-time processing on edge devices.

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

• Understand the fundamentals of Edge AI and its applications in computer vision.
• Deploy optimized deep learning models on edge devices for real-time image and video analysis.
• Use frameworks like TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimize AI models for performance, power efficiency, and low-latency inference.

This instructor-led, live training in Slovakia (online or onsite) is aimed at beginner-level law enforcement personnel who wish to transition from manual facial sketching to using AI tools for developing facial recognition systems.

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

• Understand the fundamentals of Artificial Intelligence and Machine Learning.
• Learn the basics of digital image processing and its application in facial recognition.
• Develop skills in using AI tools and frameworks to create facial recognition models.
• Gain hands-on experience in creating, training, and testing facial recognition systems.
• Understand ethical considerations and best practices in the use of facial recognition technology.

This instructor-led, live training, offered online or onsite, is designed for developers aiming to utilize 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 required development environment integrating FastAPI, React, and MongoDB.
• Understand the core concepts, features, and benefits of the FARM stack.
• Learn how to construct REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (both front end and back end) using the FARM stack.

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

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

• Navigate the Fiji interface and utilize ImageJ’s core functions.
• Preprocess and enhance scientific images for better analysis.
• Analyze images quantitatively, including cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customize workflows for specific image analysis needs in biological research.

This instructor-led, live training in Slovakia (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 instructor-led, live training session Slovakia (available online or on-site) is tailored for intermediate to advanced-level developers, researchers, and data scientists seeking to learn how to implement real-time object detection using YOLOv7.

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

• Comprehend the fundamental concepts of object detection.
• Install and configure YOLOv7 for object detection tasks.
• Train and test custom object detection models using YOLOv7.
• Integrate YOLOv7 with other computer vision frameworks and tools.
• Troubleshoot common issues related to YOLOv7 implementation.