Computer Vision Course

Dive into the captivating realm of computer vision with our specialized course. Explore cutting-edge techniques in image processing, object detection, image segmentation, and facial recognition. Our hands-on curriculum ensures you gain practical experience in building computer vision models for real-world applications. Join us to unlock the potential of visual intelligence and revolutionize industries.

|| What will I learn?

  • Understand the fundamental principles and concepts of computer vision.
  • Process and analyze images and videos using computer vision techniques.
  • Implement algorithms for feature detection and matching.
  • Develop methods for object recognition and classification.
  • Apply deep learning techniques to computer vision tasks.
  • Solve real-world problems using computer vision technologies.

|| What will I learn?

  • Understand the fundamental principles and concepts of computer vision.
  • Process and analyze images and videos using computer vision techniques.
  • Implement algorithms for feature detection and matching.
  • Develop methods for object recognition and classification.
  • Apply deep learning techniques to computer vision tasks.
  • Solve real-world problems using computer vision technologies.

|| Requirements

  • Proficiency in a programming language (Python preferred)
  • Familiarity with machine learning concepts
  • Basic understanding of linear algebra, calculus, and probability

|| Requirements

  • Proficiency in a programming language (Python preferred)
  • Familiarity with machine learning concepts
  • Basic understanding of linear algebra, calculus, and probability

    • Computer Vision
    • Image Formation
    • Sampling and Quantisation
    • Image Processing – flipping, cropping, rotating, scaling
    • Image statistics & Histogram
    • Spatial Resolution
    • Gray level/Intensity Resolution
    • Spatial Filtering
    • Convolution
    • Smoothing, Sharpening
    • Color Space Conversion & Histogram


    • Thresholding for Binarization
    • Morphological Operations
    • Image Gradient
    • Bounding Box
    • Sobel’s Edge Detection Operator
    • Template Matching
    • Image Feature – Keypoint and Descriptor
    • Harris Corner Detector
    • Object Detection with HoG
    • Stream Video Processing with OpenCV

    • Computer Vison
    • Convolution Neural Networks (CNN)
    • Why CNN? Building an Intuition for CNN
    • CNN, Kernels, Channels, Feature Maps, Stride, Padding
    • Receptive Fields, Image Output Dimensionality Calculations, MNIST Dataset
    • Explorations with CNN
    • MNIST CNN Intuition, Tensorspace.js, CNN Explained, CIFAR 10 Dataset Explorations with CNN
    • Dropout & Custom Image Classification for Cat and Dog Datasets
    • Deployment in Heroku, AWS or Azure


    • CNN Architectures
    • LeNet-5
    • AlexNet, VGGNet
    • Inception, ResNet
    • Data Augmentation
    • Benefits of Data Augmentation
    • Exploring Research Papers
    • Exploring Augmentor


    • Object Detection Basics
    • What is Object Detection?
    • Competitions for Object Detection
    • Bounding Boxes
    • Bounding Box Regression
    • Intersection over Union (IoU)
    • Precision & Recall
    • What is Average Precision?
    • Practical Training using Tensorflow1.x
    • Custom Model Training in TFOD1.x
    • Our Custom Dataset
    • Doing Annotations or labelling data
    • Pretrained Model from Model Zoo
    • Files Setup for Training
    • Export Frozen Inference Graph
    • Inferencing with our trained model in Colab, Training in Local
    • Inferencing with our trained model in Local


    • Practical Training using Tensorflow2.x
    • Introduction to TFOD2.x
    • Using the Default Colab Notebook
    • Google Colab & Drive Setup


    • Visiting TFOD2.x Model Garden
    • Inference using Pretrained Model
    • Inferencing in Local with a pretrained model


    • Practical Object Detection Using YOLO V5
    • Introduction for YoloV5
    • YoloV5 Google Colab Setup
    • Inferencing using Pre-Trained Model


    • Prompt Engineering
    • Why Prompt Engineering?
    • ChatGPT
    • Few Standard Definitions:
    • Label
    • Logic
    • Model Parameters (LLM Parameters)
    • Basic Prompts and Prompt Formatting
    • Elements of a Prompt:
    • Context
    • Task Specification
    • Constraints
    • General Tips for Designing Prompts:
    • Be Specific
    • Keep it Concise
    • Be Contextually Aware
    • Test and Iterate
    • Prompt Engineering Use Cases
    • Information Extraction
    • Text Summarization
    • Question Answering
    • Code Generation
    • Text Classification
    • Prompt Engineering Techniques
    • N-shot Prompting
    • Zero-shot Prompting
    • Chain-of-Thought (CoT) Prompting
    • Generated Knowledge Prompting

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|| Frequently asked question

A Computer Vision Course is designed to teach participants the principles, techniques, and applications of computer vision, a field of artificial intelligence that enables computers to interpret and understand visual information from the real world, such as images and videos.

This course is suitable for students, professionals, and enthusiasts interested in gaining expertise in computer vision. It caters to individuals with varying levels of experience, from beginners with no prior knowledge to experienced practitioners looking to specialize in computer vision techniques.

Prerequisites may include a basic understanding of programming concepts, familiarity with linear algebra, calculus, and probability theory, and proficiency in a programming language such as Python. Some courses may also require prior knowledge of machine learning concepts and techniques.

Most reputable Computer Vision courses offer a certificate of completion, which can enhance your credentials and be added to your resume or LinkedIn profile. It's essential to verify the accreditation and recognition of the issuing institution or organization.

Some courses offer job placement assistance or career services, including resume building, interview preparation, and networking opportunities with industry professionals. However, this varies depending on the course provider and the course's focus.

Yes, many Computer Vision courses are available online, offering flexibility in terms of timing and location. Online courses often provide video lectures, interactive exercises, and discussion forums to facilitate learning.

Yes, Computer Vision courses typically include hands-on projects, case studies, and practical exercises to apply the concepts learned to real-world problems. This practical experience is essential for developing proficiency and building a portfolio of projects.

After completing the course, you may continue to have access to course materials, online resources, alumni networks, career services, and professional development opportunities to support your continued learning and career growth.

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