YOLO-NAS + v8 Full-Stack Computer Vision Course from Coursera

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This comprehensive course introduces you to the YOLO-NAS + v8 model and its integration into real-world applications. You’ll gain hands-on experience in using YOLO-NAS + v8 for object detection, training custom models, and deploying solutions across different platforms, from cloud services to mobile devices. Learn how to transition from YOLOv8 to YOLO-NAS, refine object detection capabilities, and integrate advanced tracking techniques with algorithms like DeepSORT and Bytetrack.

The course takes you on a journey through every aspect of YOLO-NAS + v8, from setting up the environment on various platforms, fine-tuning models for specific needs, and exploring use cases such as waste sorting detection and safety compliance. You’ll also learn model conversion techniques and deploy solutions on Docker, Jetson NANO, and even mobile devices using Kivy.

Whether you're looking to enhance your computer vision skills or integrate AI into mobile and web apps, this course caters to all levels of learners. If you're a developer, data scientist, or researcher, this course will provide you with the necessary tools to create sophisticated AI-driven applications across a wide range of industries.

What's inside

Syllabus

YOLO-NAS + v8 Introduction

In this module, we will introduce you to YOLO-NAS + v8, exploring its advancements and features over previous versions. You will learn how to set up and run YOLO-NAS + v8 across different platforms, including Google Colab. The module also covers essential use cases, hands-on applications, and techniques for fine-tuning pre-trained models for optimal performance.

Career center

Learners who complete YOLO-NAS + v8 Full-Stack Computer Vision Course will develop knowledge and skills that may be useful to these careers:

Computer Vision Engineer

A Computer Vision Engineer is at the forefront of developing systems that enable machines to interpret and understand visual information from the world, much like human sight. This course provides comprehensive training essential for this role, covering core concepts from object detection, tracking, and segmentation using advanced models like YOLO-NAS and YOLOv8. Learners will gain direct experience in fine-tuning pre-trained models, working with custom datasets, and applying data augmentation techniques crucial for robust vision systems. The curriculum's focus on real-time tracking with algorithms such as DeepSORT and deployment across varied platforms, including cloud services, mobile devices, and edge computing via Jetson NANO, directly prepares you for the practical challenges encountered by a Computer Vision Engineer, ensuring you can build sophisticated AI-driven applications across industries.

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Deep Learning Engineer

A Deep Learning Engineer specializes in designing, implementing, and optimizing neural network architectures for various AI tasks. This course is an excellent fit, focusing intensely on the YOLO-NAS plus v8 model, a state-of-the-art deep learning architecture for computer vision. You will gain in-depth knowledge of its advancements and features over previous versions, learning to fine-tune pre-trained models and train custom ones for specific applications. The curriculum explores advanced techniques like object segmentation, even integrating YOLO-NAS with SAM for superior accuracy, which is a key skill for a Deep Learning Engineer. The opportunity to deploy these highly optimized models across platforms from cloud to mobile ensures you can translate theoretical knowledge into practical, performant deep learning solutions.

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Machine Learning Engineer

As a Machine Learning Engineer, you build, optimize, and deploy machine learning models into production environments. This course is highly relevant, providing hands-on experience in training custom YOLO-NAS plus v8 models, a fundamental task for any Machine Learning Engineer. It delves into the entire model lifecycle, from data pre-processing and augmentation to model conversion for various formats like TensorFlow and TensorRT, which is critical for optimizing performance across different hardware. Furthermore, the course's emphasis on deployment strategies for Docker, mobile platforms using Kivy, and web applications with Flask equips you with the full-stack skills needed to deliver robust, scalable AI solutions. This comprehensive approach ensures you are well-prepared to integrate advanced AI capabilities into diverse applications.

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Full Stack AI Developer

A Full Stack AI Developer builds complete AI-powered applications, handling both the backend logic and the user-facing interfaces. This course is exceptionally tailored for a Full Stack AI Developer, offering direct experience in integrating cutting-edge computer vision models like YOLO-NAS plus v8 into both web and mobile applications. You will learn to set up Flask applications to create web-based AI solutions, design intuitive front-end interfaces, and implement real-world use cases such as retail heat maps and safety compliance systems. Furthermore, the course guides you through mobile app development using Kivy, including integrating mobile object detection with TensorFlow Lite. This comprehensive approach ensures you possess the skills to build, deploy, and manage entire AI-driven applications from end to end.

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AI Solutions Architect

An AI Solutions Architect designs and oversees the implementation of complex artificial intelligence systems, ensuring they are scalable, efficient, and meet business needs. This course provides valuable insights for an AI Solutions Architect by covering the full spectrum of bringing computer vision models to life. It emphasizes deployment on diverse platforms, including cloud services, Docker, Jetson NANO for edge computing, and mobile devices, requiring a deep understanding of system integration and optimization. Learning model conversion techniques for various ecosystems such as CoreML and OpenVino is crucial for architectural decisions. The experience gained in integrating models into web (Flask) and mobile (Kivy) applications, building full-stack AI solutions, allows you to conceptualize and design end-to-end AI architectures confidently.

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Mobile Machine Learning Engineer

A Mobile Machine Learning Engineer specializes in developing and deploying machine learning models specifically for mobile devices, optimizing them for performance and battery efficiency. This course is highly beneficial for a Mobile Machine Learning Engineer, as it dedicates significant modules to mobile app development using Kivy. You will gain hands-on experience in integrating the YOLO-NAS plus v8 model for mobile object detection, crucially learning to convert models to TensorFlow Lite for optimized performance on constrained mobile hardware. The curriculum also covers creating engaging user interfaces for these AI-powered mobile apps and addresses essential deployment, testing, and debugging steps to ensure smooth operation across devices. This specialized focus directly equips you to build sophisticated AI capabilities into various mobile applications.

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MLOps Engineer

An MLOps Engineer focuses on streamlining the machine learning lifecycle, from model development to deployment, monitoring, and maintenance in production environments. This course is highly pertinent for an MLOps Engineer, offering practical experience in critical deployment strategies. You will learn how to deploy YOLO-NAS in Docker, ensuring models run efficiently in isolated and scalable environments, a cornerstone of MLOps. The curriculum also covers model conversion techniques, essential for optimizing models for various deployment targets and ensuring performance consistency. Additionally, the course's emphasis on deploying solutions across different platforms—from cloud services to mobile devices and edge computing—provides a holistic understanding of the deployment challenges and solutions that an MLOps Engineer navigates daily to ensure robust and reliable AI systems.

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Embedded Software Engineer Computer Vision

An Embedded Software Engineer Computer Vision develops and optimizes vision-based software for specialized hardware platforms, often with limited resources, such as those used in robotics or IoT. This course is invaluable for this career path, specifically addressing the deployment of YOLO-NAS on the Jetson NANO platform, focusing on optimizing performance for edge computing scenarios. You will learn the intricacies of model conversion to formats like TensorRT, which is essential for maximizing efficiency on embedded systems. The hands-on experience in setting up the environment and deploying robust object detection and tracking solutions on such platforms provides the practical skills necessary to design and implement efficient, real-time computer vision applications in constrained embedded environments, which is a core task for an Embedded Software Engineer Computer Vision.

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Automotive Computer Vision Engineer

An Automotive Computer Vision Engineer develops and integrates vision systems for vehicles, crucial for advanced driver assistance systems (ADAS) and autonomous driving. This course provides highly relevant skills for an Automotive Computer Vision Engineer. The core focus on object detection, Multi Object Tracking with algorithms like DeepSORT and Bytetrack, and object segmentation are fundamental for a vehicle's perception stack, enabling it to accurately identify and track other vehicles, pedestrians, and road signs. The emphasis on real-time tracking in challenging environments and deploying models on edge computing platforms like Jetson NANO is directly applicable to the demanding requirements of in-vehicle systems, where low latency and high accuracy are paramount for safety and reliability.

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Robotics Software Engineer

A Robotics Software Engineer is central to developing the intelligence and perception systems that allow robots to interact with their environment. This course offers highly relevant skills for a Robotics Software Engineer, particularly in the domain of computer vision. The modules on object detection, Multi Object Tracking (MOT) using algorithms like DeepSORT, and object segmentation provide the foundational capabilities for robots to perceive, understand, and navigate their surroundings. The ability to deploy models on edge computing platforms such as Jetson NANO is directly applicable to onboard robot processing. Furthermore, the course's emphasis on real-time tracking in challenging environments and creating AI-driven solutions equips you to build robust perception modules essential for advanced robotic applications.

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Applied AI Researcher

An Applied AI Researcher explores and adapts advanced artificial intelligence techniques to solve specific, real-world problems, often bridging the gap between theoretical research and practical implementation. This course is highly valuable for an Applied AI Researcher as it deeply engages with state-of-the-art models like YOLO-NAS plus v8. Learners will gain expertise in fine-tuning these models for optimal performance in various scenarios, understanding their advancements and limitations. The curriculum's exploration of advanced multi-object tracking algorithms and object segmentation, including integrating YOLO-NAS with SAM, provides hands-on methods for pushing the boundaries of current vision capabilities. The bonus content even delves into new applications like VegGPT, equipping you with cutting-edge insights to enhance and apply computer vision for novel challenges. An advanced degree is often required for this role.

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Industrial Automation Engineer

An Industrial Automation Engineer designs and implements systems to automate manufacturing and operational processes, often using sensors and control systems. This course offers practical computer vision skills highly applicable to this field. The curriculum explicitly covers use cases such as waste sorting detection and ensuring safety compliance in mining, which are direct applications of automation in industrial settings. Learners will gain the ability to deploy YOLO-NAS plus v8 models for real-time monitoring and analysis, an essential component for automated quality control, defect detection, and predictive maintenance. The course's focus on deploying solutions on platforms like Docker and Jetson NANO also aligns with implementing robust, scalable vision systems within factory or industrial environments, enabling efficient and safe operations.

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Data Scientist Computer Vision Focus

A Data Scientist with a Computer Vision Focus specializes in collecting, cleaning, analyzing, and modeling visual data to extract insights and build predictive systems. This course offers crucial practical experience for this specialization. Learners will gain hands-on expertise in the complete process of training custom YOLO-NAS plus v8 models, which includes understanding data pre-processing and augmentation techniques relevant for visual datasets. The course also covers working with custom datasets and leveraging tools like Roboflow for streamlining model training and testing. While not purely statistical, the emphasis on data preparation, model evaluation, and understanding the performance of vision models are all vital skills that a Data Scientist Computer Vision Focus would apply to develop and improve AI systems.

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Security Systems Engineer

A Security Systems Engineer designs, implements, and maintains security infrastructure, often incorporating surveillance and threat detection technologies. This course may be useful for a Security Systems Engineer. The modules on object detection and Multi Object Tracking (MOT) are directly applicable to enhancing surveillance capabilities, allowing for automated monitoring and anomaly detection. Learning to create a real-time tracking and analytics dashboard using Streamlit, as covered in the course, provides valuable skills for visualizing security data. Furthermore, the development of mobile applications for use cases like people counting and the ability to deploy computer vision solutions across various platforms can significantly bolster the intelligent capabilities of modern security systems, enabling more proactive and efficient security operations.

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Augmented Reality Developer

An Augmented Reality Developer creates interactive digital experiences that overlay virtual content onto the real world, relying heavily on understanding the physical environment. This course may be useful for an Augmented Reality Developer. While not explicitly focused on AR, the fundamental computer vision skills acquired—such as object detection, tracking, and segmentation—are absolutely critical for AR applications to accurately perceive and register virtual objects within a real-world scene. The ability to deploy models across mobile devices using Kivy and to process real-time video streams provides a strong foundation for building performant AR experiences. Understanding how to interpret visual data is key to world tracking and interaction, which are core components of developing immersive augmented reality applications.

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