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Courses Computer Vision using Deep Learning

Computer Vision using Deep Learning

Whether you want to build systems that can classify images, detect and locate objects, segment visual scenes, or even generate entirely new images, this course gives you the practical skills to do it all. Starting with the foundations of convolutional neural networks, you'll progressively work through the core pillars of modern computer vision — image classification, object detection with architectures like YOLO and Faster R-CNN, and image segmentation with models like U-Net and Mask R-CNN — before diving into generative AI with Stable Diffusion, including techniques like inpainting, ControlNet, and LoRA adapters.

Every topic is taught hands-on using industry-standard tools like PyTorch, Torchvision, Albumentations, Ultralytics, and Hugging Face Diffusers, and the course wraps up with mini projects that challenge you to combine everything into complete, end-to-end solutions.

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What will I learn?

Understand the core concepts behind convolutional neural networks and how they process visual data.
Build, train, and evaluate image classification models using PyTorch and PyTorch Lightning.
Apply image augmentation techniques to improve model performance and generalization.
Distinguish between one-stage and two-stage object detection architectures and know when to use each.

Run object detection on images and videos using state-of-the-art models like YOLO and Faster R-CNN.
Perform image segmentation using models such as U-Net, Mask R-CNN, and SAM.
Generate and manipulate images using Stable Diffusion with techniques like inpainting, ControlNet, and LoRA adapters.
Combine multiple computer vision models into end-to-end solutions through hands-on mini projects.

Curriculum

Introduction to Computer Vision

An overview of the computer vision landscape, image classification, image processing with OpenCV, and data augmentation techniques using Albumentations.

Convolutional Neural Networks

A deep dive into CNN architecture, from high-level intuition to low-level details, followed by building and training a CNN from scratch in PyTorch.

Introduction to Object Detection

An introduction to the object detection problem, key concepts, and the evolution of approaches used to locate and classify objects within images.

Two-Stage Object Detectors

A look at two-stage detection architectures like Faster R-CNN that first propose candidate regions and then classify them.

Single-Stage Object Detectors

An exploration of faster, single-stage architectures like YOLO and SSD that perform detection in a single pass through the network.

Image Segmentation

An overview of segmentation techniques and models including U-Net, Mask R-CNN, DeepLab, and SAM for pixel-level image understanding.

Generative AI

An introduction to generative models in computer vision, covering diffusion models, Stable Diffusion, and techniques like inpainting, ControlNet, and LoRA adapters.

Mini Projects

Hands-on projects that bring everything together, including object detection and segmentation pipelines, multiclass classification, and image generation with Stable Diffusion.

Why Edlitera?

Build the coding, data and AI skills you need, online, on your own schedule. From learning to code as a beginner to mastering cutting-edge data science, machine learning and AI techniques.

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Learning for the real world

Our courses are made with the input and feedback of top teams at Fortune 500 companies in Silicon Valley and on Wall Street.

No-fluff learning

Each minute of each course is packed full of insight, best practices and real-world experience from our expert instructors.

Learn by doing

Start writing code on your computer from Day One. Practice on hundreds of exercises. Apply your skills in mini-projects. Get instant feedback from video solutions.

Complete learning tracks

With over 150 hours of video lectures and hundreds of practice exercises and projects, our learning tracks will help you level up your skills whether you are a novice or an advanced learner.

What people are saying

"I walked into the bootcamp with some basic Python syntax and walked out with a much stronger, contextualized grasp of Python, an understanding of common mistakes, the ability to solve basic coding problems, and confidence in my ability to learn more."

Randi S., Edlitera Student

Randi S., a graduate of Edlitera's Python training bootcamp

"I wanted to learn Python and be able to process data without being tied and limited by Excel and macros. These classes gave me all the tools to do so and beyond. The materials provided, the engagement of the class by the tutors and their availability to help us were excellent."

Gaston G., Edlitera Student

Gaston G., a graduate of Edlitera's Python training bootcamp