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 create maintainable, testable, and high-performance Python applications. The curriculum emphasizes modern tooling, type hinting, concurrency models, architectural patterns, and deployment-ready workflows.

Offered as instructor-led live training (available online or on-site), this program is tailored for intermediate to advanced Python developers looking to adopt professional practices and patterns for production-grade Python systems.

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

• Apply 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 with pytest, property-based testing, and CI pipelines.
• Profile, optimize, and secure Python applications for production environments.
• Package, distribute, and deploy Python projects using contemporary tools and containers.

Course Format

• Interactive lectures complemented by short demonstrations.
• Hands-on labs and coding exercises every day.
• A capstone mini-project integrating patterns, testing, and deployment.

Course Customization Options

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

This course imparts practical engineering techniques for designing, building, testing, and deploying autonomous agentic systems using Python. It explores the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production readiness.

Delivered as an instructor-led, live training (available online or onsite), this program targets intermediate to advanced ML engineers, AI developers, and software engineers seeking to construct robust, production-ready autonomous agents with Python.

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

• Design and implement the agent loop along with 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 agent composability.
• Apply safety, access control, and observability best practices for 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 customized training for this course, please contact us to make arrangements.

This course provides a comprehensive exploration of practical Data Science and Artificial Intelligence techniques using Python. It empowers professionals with the essential skills to investigate data, construct machine learning models, and implement AI-driven solutions within business environments. The curriculum covers CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. It is ideally suited for beginners pursuing a Python data science certification and those seeking career-ready analytics training.

Artificial Intelligence with Python focuses on creating intelligent systems by leveraging Python’s comprehensive ecosystem of AI and machine learning libraries.

Delivered through an instructor-led live training format, available either online or onsite, this program is designed for intermediate Python developers who aim to design, implement, and deploy AI solutions.

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

• Develop AI algorithms utilizing Python’s core AI libraries.
• Utilize supervised, unsupervised, and reinforcement learning models.
• Seamlessly integrate AI solutions into current applications and workflows.
• Assess model performance and optimize them for both accuracy and efficiency.

Course Format

• Engaging lectures and interactive discussions.
• Ample opportunities for exercises and practical application.
• Hands-on implementation within a live-lab environment.

Customization Options

• For those interested in a customized training version of this course, please get in touch with us to make arrangements.

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) targets intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

Upon completing this training, participants will be able to:

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into live pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Develop real-time CV/NLP pipelines for both edge and cloud-based deployment scenarios.

Course Format

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

Customization Options

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

This instructor-led, live training in Kenya (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 delivered Kenya (online or onsite) is designed for advanced-level professionals aiming to deepen their grasp of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the conclusion 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, live training in Kenya (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 Kenya (online or onsite) targets computer vision engineers, AI developers, and IoT professionals at an intermediate to advanced level who aim to implement and optimize computer vision models for real-time processing on edge devices.

Upon completing this training, participants will be able to:

• Grasp the core concepts of Edge AI and how it applies to 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 better performance, power efficiency, and low-latency inference.

This instructor-led, live training in Kenya (online or on-site) is targeted 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 conclusion 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, available online or onsite, targets developers who wish 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 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 Kenya (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 session in Kenya (online or onsite) is designed for beginner to intermediate 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 Kenya (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 in Kenya (online or onsite) is designed for intermediate to advanced developers, researchers, and data scientists who want to learn how to implement real-time object detection using YOLOv7.

Upon completing this training, participants will be equipped to:

• Grasp the core principles underlying object detection.
• Install and set up YOLOv7 for various object detection tasks.
• Train and evaluate custom object detection models leveraging YOLOv7.
• Seamlessly integrate YOLOv7 with other computer vision frameworks and tools.
• Diagnose and resolve common challenges associated with YOLOv7 deployment.