Computer Vision with Python Training Course

This instructor-led, live training in Latvia explores advanced Python techniques, engineering best practices, and design patterns. It supports developers in building maintainable, testable, and high-performance applications by leveraging modern tooling, typing, and concurrency models for production-grade systems.

This instructor-led live training in Latvia offers practical Python techniques for developing autonomous agents. It covers essential components such as agent loops, tool integration, memory management, orchestration, and safety controls. Designed for ML engineers, the focus is on deploying robust, production-grade agents.

This live, instructor-led training, available Latvia (online or onsite), targets intermediate AI developers and computer vision engineers who aim to develop robust vision systems for autonomous driving.

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

• Comprehending the fundamental principles of computer vision in autonomous vehicles.
• Implementing algorithms for object detection, lane detection, and semantic segmentation.
• Integrating vision systems with other autonomous vehicle subsystems.
• Utilizing deep learning techniques for complex perception tasks.
• Evaluating the performance of computer vision models in real-world scenarios.

This live, instructor-led training in Latvia (online or onsite) is tailored for advanced professionals aiming to refine their computer vision knowledge and harness TensorFlow’s power to build sophisticated vision models via Google Colab.

By the conclusion of this program, participants will have the ability to:

• Develop and train convolutional neural networks (CNNs) with TensorFlow.
• Harness Google Colab for efficient and scalable cloud-based model creation.
• Apply image preprocessing methods to support computer vision tasks.
• Launch computer vision models into production for real-world use.
• Integrate transfer learning to optimize CNN model performance.
• Analyze and visualize the results of image classification models.

This instructor-led live training in Latvia, offered either online or onsite, is tailored for intermediate-level Python developers and data analysts aiming to strengthen their data analysis and manipulation skills using Pandas and NumPy.

By the end of this session, 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 Latvia (online or onsite) targets intermediate to advanced computer vision engineers, AI developers, and IoT professionals. It focuses on the implementation and optimization of computer vision models for real-time processing on edge devices.

By the end of this program, participants will have the ability 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 Latvia (online or onsite) is designed for entry-level law enforcement professionals seeking to move away from manual facial sketching towards developing AI-based facial recognition systems.

Following this training, participants will have the ability to:

• Comprehend the core principles of Artificial Intelligence and Machine Learning.
• Master the basics of digital image processing and its role in facial recognition.
• Develop proficiency in using AI tools and frameworks to build facial recognition models.
• Obtain hands-on experience in the creation, training, and testing of facial recognition systems.
• Understand the ethical implications and best practices for deploying facial recognition technology.

This live, instructor-led training, offered online or onsite, targets developers aiming to utilize the FARM (FastAPI, React, and MongoDB) stack to create dynamic, high-performance, and scalable web applications.

By the conclusion of this program, participants will have the capability to:

• Configure a development environment that seamlessly integrates FastAPI, React, and MongoDB.
• Comprehend the essential concepts, functionalities, and benefits associated with the FARM stack.
• Implement REST APIs utilizing FastAPI.
• Build interactive applications using React.
• Manage the development, testing, and deployment of applications, including both front-end and back-end elements, through the FARM stack.

This live, instructor-led training in Latvia (delivered online or on-site) is tailored for developers aiming to utilize FastAPI alongside Python to construct, test, and deploy RESTful APIs with enhanced ease and velocity.

By the conclusion of this session, participants will be equipped to:

• Configure the essential development environment for API development using Python and FastAPI.
• Streamline the creation of APIs by applying the FastAPI library.
• Gain proficiency in designing data models and schemas using Pydantic and OpenAPI.
• Link APIs to database systems using SQLAlchemy.
• Enforce security and authentication mechanisms in APIs through FastAPI tools.
• Generate container images and execute the deployment of web APIs to cloud servers.

This practical training in Latvia establishes a strong foundation in Fiji and ImageJ for biological research applications. Participants will acquire essential skills in image preprocessing, quantitative analysis, and macro automation, enabling them to optimize workflows for histological tissues, cells, and other samples while ensuring reproducible and reliable results.

This instructor-led, live training conducted in Latvia (online or onsite) is tailored for intermediate-level professionals aiming to utilize Vision Builder AI for the design, implementation, and optimization of automated inspection systems within SMT (Surface-Mount Technology) environments.

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

• Set up and configure automated inspection procedures using Vision Builder AI.
• Acquire and preprocess high-quality images for analytical purposes.
• Implement logic-based decision frameworks for detecting defects and validating processes.
• Generate comprehensive inspection reports and optimize system performance.

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

Upon completion, participants will be able to:

• Understand 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.