Online Computer Vision Training in Botswana

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

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

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

This instructor-led, live training in Botswana (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.

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.

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization tools 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 designed for intermediate-level AI practitioners who want to build, deploy, and optimize vision and language models for production environments using the CANN SDK.

By the conclusion 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.
• Optimize inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines for both edge and cloud-based deployment scenarios.

Course Format

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

Course Customization Options

• To request a customized training session for this course, please contact us to make arrangements.

This instructor-led, live training in Botswana (online or onsite) is aimed at intermediate-level AI developers and computer vision engineers who wish 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 Botswana (online or in-person) is designed for beginner-level law enforcement officers who wish to move from manual facial sketching to using AI tools for developing facial recognition systems.

Upon completing this training, participants will be able to:

• Grasp the core principles of Artificial Intelligence and Machine Learning.
• Learn the fundamentals of digital image processing and its role in facial recognition.
• Acquire skills in utilizing AI tools and frameworks to construct facial recognition models.
• Gain practical experience in building, training, and evaluating facial recognition systems.
• Understand the ethical implications and best practices associated with facial recognition technology.

This instructor-led live training in Botswana (online or onsite) is aimed at beginner to intermediate researchers and laboratory professionals who wish to process and analyse 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 Botswana (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 in Botswana (online or onsite) targets intermediate to advanced developers, researchers, and data scientists keen on mastering the implementation of real-time object detection using YOLOv7.

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

• Grasping the core concepts of object detection.
• Installing and configuring YOLOv7 for object detection tasks.
• Training and evaluating custom object detection models with YOLOv7.
• Integrating YOLOv7 with other computer vision frameworks and tools.
• Resolving common issues associated with YOLOv7 implementation.

Fiji is a powerful open-source image processing suite that packages ImageJ (a programme designed for scientific multi-dimensional images) along with a comprehensive suite of plugins for scientific image analysis.

In this instructor-led live training, participants will learn how to leverage the Fiji distribution and its underlying ImageJ programme to create robust image analysis applications.

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

• Use Fiji's advanced programming features and software components to extend ImageJ capabilities
• Stitch large 3D images from overlapping tiles
• Automate the update of a Fiji installation on startup using the integrated update system
• Select from a broad selection of scripting languages to build custom image analysis solutions
• Utilise Fiji's powerful libraries, such as ImgLib, to process large bioimage datasets efficiently
• Deploy applications and collaborate effectively with other scientists on similar projects

Format of the Course

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

Course Customisation Options

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

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source library licensed under the BSD license, featuring several hundred computer vision algorithms.

Target Audience

This course is aimed at engineers and architects who wish to leverage OpenCV for computer vision projects.

This instructor-led, live training in Botswana (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

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

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.

Format of the Course

• This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.

Computer Vision is a discipline focused on the automatic extraction, analysis, and interpretation of valuable information from digital media. Python is a high-level programming language renowned for its clear syntax and code readability.

In this instructor-led live training, participants will master the fundamentals of Computer Vision by building a set of simple Computer Vision applications using Python.

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

• Understand the basics of Computer Vision
• Use Python to implement Computer Vision tasks
• Build their own face, object, and motion detection systems

Audience

• Python programmers interested in Computer Vision

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

SimpleCV is an open-source framework, which means it consists of a suite of libraries and software tools that you can utilise to develop vision applications. It allows you to work with images or video streams from webcams, Kinect sensors, FireWire cameras, IP cameras, or mobile phones. It assists you in building software that enables your technologies to not only see the world but also understand it.

Audience

This course is aimed at engineers and developers who wish to develop computer vision applications using SimpleCV.

This instructor-led, live training Botswana (online or onsite) is designed for backend developers and data scientists who wish to integrate pre-trained YOLO models into their enterprise applications and implement cost-effective components for object detection.

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

• Install and set up the necessary tools and libraries required for object detection using YOLO.
• Customize Python command-line applications that leverage YOLO pre-trained models.
• Deploy the pre-trained YOLO model framework for various computer vision projects.
• Transform existing object detection datasets into the YOLO format.
• Grasp the core concepts of the YOLO algorithm within the contexts of computer vision and/or deep learning.