AI for Transportation

This course covers the range of ways that AI can be used to better perform common tasks within the logistics business, including inventory management and demand prediction.

This course offers a comprehensive overview of how AI technologies are reshaping transportation industries on land, sea, and air, covering autonomous vehicles, public transit optimization, and more.

An online course to promote the responsible use of AI in transportation, equipping practitioners with knowledge of AI technologies and policy considerations to improve safety, mobility, and efficiency.

This course provides a comprehensive exploration of the transformative role that AI plays in modern transportation systems. It covers topics like AI for Traffic Management and Optimization and Autonomous Vehicles.

Welcome to the course "Modern Computer Vision & Deep Learning with Python & PyTorch"! Imagine being able to teach computers to see just like humans. Computer Vision is a type of artificial intelligence (AI) that enables computers and machines to see the visual world, just like the way humans see and understand their environment. Artificial intelligence (AI) enables computers to think, where Computer Vision enables computers to see, observe and interpret. This course is particularly designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to major Computer Vision problems including Image Classification, Semantic Segmentation, Instance Segmentation, and Object Detection. In this course, you'll start with an introduction to the basics of Computer Vision and Deep Learning, and learn how to implement, train, test, evaluate and deploy your own models using Python and PyTorch for Image Classification, Image Segmentation, and Object Detection. Computer Vision plays a vital role in the development of autonomous vehicles. It enables the vehicle to perceive and understand its surroundings to detect and classify various objects in the environment, such as pedestrians, vehicles, traffic signs, and obstacles. This helps to make informed decisions for safe and efficient vehicle navigation. Computer Vision is used for Surveillance and Security using drones to track suspicious activities, intruders, and objects of interest. It enables real-time monitoring and threat detection in public spaces, airports, banks, and other security-sensitive areas. Today Computer Vision applications in our daily life are very common including Face Detection in cameras and cell phones, logging in to devices with fingerprints and face recognition, interactive games, MRI, CT scans, image guided surgery and much more. This comprehensive course is especially designed to give you hands-on experience using Python and PyTorch coding to build, train, test and deploy

This course is designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to Semantic Image Segmentation problems. Are you ready to take your understanding of deep learning to the next level and learn how to apply it to real-world problems? In this course, you'll learn how to use the power of Deep Learning to segment images and extract meaning from visual data. You'll start with an introduction to the basics of Semantic Segmentation using Deep Learning, then move on to implementing and training your own models for Semantic Segmentation with Python and PyTorch.This course is designed for a wide range of students and professionals, including but not limited to:Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Image Segmentation tasks Computer Vision Engineers and Researchers who want to learn how to use PyTorch to build and train Deep Learning models for Semantic Segmentation Developers who want to incorporate Semantic Segmentation capabilities into their projects Graduates and Researchers in Computer Science, Electrical Engineering, and other related fields who want to learn about the latest advances in Deep Learning for Semantic Segmentation In general, the course is for Anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorch The course covers the complete pipeline with hands-on experience of Semantic Segmentation using Deep Learning with Python and PyTorch as follows:Semantic Image Segmentation and its Real-World Applications in Self Driving Cars or Autonomous Vehicles etc.De

Are you interested in unlocking the full potential of Artificial Intelligence? Do you want to learn how to create powerful image recognition systems that can identify objects with incredible accuracy? If so, then our course on Deep Learning with Python for Image Classification is just what you need! In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models and Transfer Learning. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques. Embark on a journey into the fascinating world of deep learning with Python and PyTorch, tailored specifically for image classification tasks. In this hands-on course, you'll delve deep into the principles and practices of deep learning, mastering the art of building powerful neural networks to classify images with remarkable accuracy. From understanding the fundamentals of convolutional neural networks&nbsp; to implementing advanced techniques using PyTorch, this course will equip you with the knowledge and skills needed to excel in image classification projects.Deep learning has emerged as a game-changer in the field of computer vision, revolutionizing image classification tasks across various domains. Understanding how to leverage deep learning frameworks like PyTorch to classify images is crucial for professionals and enthusiasts alike. Whether you're a data scientist, software engineer, researcher, or student, proficiency in deep learning for image classification opens doors to a wide range of career opportunities. Moreover, with the exponential growth of digital imagery in fields such as healthcare, autonomous vehicles, agriculture, and more, the demand for experts in image classification continues to soar.Course Breakdown:You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models. </l

Autonomous agents, an intriguing advancement in the realm of artificial intelligence, are on the brink of reshaping our work dynamics and technological interactions. These intelligent entities transcend the role of mere tools; they function as digital collaborators capable of independently managing tasks to achieve specific objectives. Whether given vague directives or precise goals like creating a sales tracker tool, these agents autonomously navigate the task at hand, continually improving their efficiency until the desired outcome is achieved. This level of automation is revolutionary, akin to an indefatigable and highly efficient worker.Accessible to individuals with coding skills, operational autonomous agents are capable of handling diverse tasks, from app development to everyday chores, thereby saving valuable time and resources. Their potential lies in transforming industries, automating mundane tasks, and freeing individuals to focus on more creative pursuits.A notable project in the field of autonomous agents is Microsoft Research's Auto Gen. This innovative tool simplifies the development of conversational agents designed to solve problems through interactions with other agents, humans, and tools. The process involves defining conversable agents and interaction behaviors, analogous to scripting a play where the user determines how agents engage in and progress through the conversation.Auto Gen's agents possess the ability to interact and collaborate, essentially functioning as a team. Leveraging Language Models (LL Ms), human input, and tools, these agents understand language, generate ideas, and make logical decisions. The central role of LL Ms supports various agent configurations, including those fine-tuned on private data. Developers can adjust human participation levels, and tools act as specialized utilities to overcome LLM limitations.Auto Gen distinguishes itself with features like unified conversation interfaces, facilitatin

Are you ready to dive into the fascinating world of object detection using deep learning? In our comprehensive course "Deep Learning for Object Detection with Python and PyTorch", we will guide you through the essential concepts and techniques required to detect, classify, and locate objects in images. Object Detection has wide range of potential real life application in many fields. Object detection is used for autonomous vehicles to perceive and understand their surroundings. It helps in detecting and tracking pedestrians, vehicles, traffic signs, traffic lights, and other objects on the road. Object Detection is used for surveillance and security using drones to identify and track suspicious activities, intruders, and objects of interest. Object Detection is used for traffic monitoring, helmet and license plate detection, player tracking, defect detection, industrial usage and much more.With the powerful combination of Python programming and the PyTorch deep learning framework, you'll explore state-of-the-art algorithms and architectures like R-CNNs, Fast RCNN and Faster R-CNNs. Throughout the course, you'll gain a solid understanding of Convolutional Neural Networks (CNNs) and their role in Object Detection. You'll learn how to leverage pre-trained models, fine-tune them for Object Detection using Detectron2 Library developed by by Facebook AI Research (FAIR).The course covers the complete pipeline with hands-on experience of Object Detection using Deep Learning with Python and PyTorch as follows:Course Break Down:Learn Object Detection with Python and PyTorch Coding Learn Object Detection using Deep Learning Models Introduction to Convolutional Neural Networks (CNNs)Learn RCNN, Fast RCNN, Faster RCNN, Mask RCNN and YOLO8, YOLO11 Architectures Perform Object Detection with Fast RCNN and Faster RCNN Perform Real-time Video Object Detection with YOL Ov8 and YOLO11</