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Introduction to Computer Vision

Standard Course
Intermediate
Coming Soon

About the Course

Explore core concepts and techniques in computer vision, including image processing, CNNs, transfer learning, and generative models. Through hands-on projects, gain practical skills to develop and optimize vision-based solutions for real-world applications.

Learning Outcomes

By the end of this course, participants will be able to:

  • Understand Image Fundamentals on how digital images are formed, represented, and transformed.
  • Apply Classical Image Processing Techniques by implementing methods for geometric transformations, intensity adjustments, filtering, morphological operations, and color processing.
  • Explain the principles of CNN and implement them for image recognition tasks.
  • Evaluate Image Classification Models by using CV architectures
  • Apply CV techniques to solve real-world problems (e.g., Object Detection and Semantic Segmentation).

Curriculum

  • Module 1: Image Processing Basics

    Overview:

    Introduces the fundamentals of digital image processing, including how images are loaded, represented, and quantized.

    Topics to Cover:

    • Fundamentals of image representation, loading, sampling, and quantization
  • Module 2: Geometric Transformations

    Overview:

    Introduces the fundamentals of digital image processing, including how images are loaded, represented, and quantized.

    Topics to Cover:

    • Techniques for scaling, rotation, translation, and affine transforms.
  • Module 3: Intensity Transformations

    Overview:

    Explains how to modify image intensity for enhancement and preprocessing.

    Topics to Cover:

    • Histogram operations, thresholding, and contrast adjustments.

  • Module 4: Neighborhood Transformations

    Overview:

    Focuses on filtering techniques using local neighbourhoods to enhance or detect features in images.

    Topics to Cover:

    • Filtering, blurring, sharpening, denoising, and edge detection.

  • Module 5: Frequency Domain Filtering

    Overview:

    Introduces frequency-based filtering methods for image analysis and enhancement.

    Topics to Cover:

    • Fourier transforms, blurring, edge detection, and selective filtering.

  • Module 6: Morphological Operations

    Overview:

    This module delves into advanced topics such as Generative Adversarial Networks (GANs), diffusion models, and synthetic data generation.

    Tools to Cover:

    • Erosion, dilation, and applications in binary image processing.

  • Module 7: Color Processing

    Overview:
    Explains colour models and transformations for colour-based image processing.

    Topics to Cover:

    • Color spaces, conversions, and color-based processing.
  • Module 8: Image Representation

    Overview:
    Focuses on different ways to represent images for computer vision tasks.

    Topics to Cover:

    • Encoding, features, and efficient representation of images.
  • Module 9: From Neural Networks (NN) to Convolutional Neural Networks (CNN)

    Overview:
    Introduces CNNs and their role in computer vision, including kernels, convolution, and implementation.

    Topics to Cover:

    • Introduction to convolutional neural networks, kernels, and Conv2D operations.
  • Module 10: Image Classification & Recognition

    Overview:
    Covers deep learning architectures for image classification and recognition.

    Topics to Cover:

    • Deep learning architectures: LeNet, AlexNet, VGG, ResNet, Inception, EfficientNet.
  • Module 11: Object Detection

    Overview:
    Explains methods and architectures for detecting and localizing objects in images.

    Topics to Cover:

    • Methods and models: R-CNN, SSD, YOLO, RetinaNet.
  • Module 12: Semantic Segmentation

    Overview:
    Covers segmentation techniques for pixel-level classification of images.

    Topics to Cover:

    • Pixel-level classification using FCN, U-Net, DeepLab, PSPNet, and Mask R-CNN.

Tools

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STUDENT REVIEWS

What our graduates are saying

I had an incredible experience at this data science bootcamp! Coming in with no prior knowledge, I was amazed at how quickly I gained a solid understanding of the field. The curriculum is intensive and hands-on, allowing me to learn real, applicable skills through working on real client projects. The staff are exceptionally supportive and always ready to help. This bootcamp truly transformed my career path, and I couldn’t be more grateful for the knowledge and confidence I’ve gained. Highly recommended for anyone looking to break into data science!

My experience was awesome. I was taught by professional data engineers who shared their insights based on their professional experience. The topics covered are relevant to what employers are looking for and I enjoyed hands-on projects. I learned all the skills I needed to be a data engineer and I got a full-time offer as a data engineer, thanks to the mentors from WecloudData. I highly recommend this program. I was able to land interviews at Amazon, Yelp, CI Financial e.t.c

Deciding to go with WeCloudData was one of the best decisions I made during my career switch. Their Bootcamps are intense but they get you where you need to go. The instructors are amazing and the TAs very knowledgeable. Their client projects taught me a lot even after the bootcamp was done. I was able to land my current job thanks to the skills and training from the course.

I had the pleasure of attending WeCloudData’s Data Fundamentals bootcamp, which was fantastic. The highly qualified instructors used cutting-edge teaching techniques that greatly facilitated understanding the logic of coding and programming. They carefully progressed us from fundamental to more complex subjects, avoiding any feeling of overload. I wholeheartedly endorse this course for anyone hoping to become a data analyst—it’stheonly one you’ll need. The teachers are very outstanding!

This bootcamp provided me with the necessary technical skills and industry knowledge to secure a data job. The instructors are knowledgeable and shared everything they know to help me achieve my goals. The TAs are resourceful and patient answering my questions. The projects in the bootcamp enhanced my knowledge and impressed interviews. This is a wonderful bootcamp if you are looking to either imporve your skills or switch career!

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Common Questions

Find answers to your questions about the Learning Track
  • Standard Courses: Focused, short courses that build foundational or intermediate skills through hands-on exercises, enabling you to apply what you learn immediately.
  • Track Courses: Structured learning paths that guide you from beginner to advanced levels. They include practical projects that integrate multiple tools and workflows, aligned with industry best practices, helping you gain the skills and confidence to tackle real-world challenges.

No. Track Courses are only accessible through the Professional or Unlimited+ subscription plans.

  • Standard Plan gives you access to all Standard Courses.
  • Professional Plan gives you access to both Standard and Track Courses within your chosen domain.
  • Unlimited+ Plan provides full access to all courses — both Standard and Track — across all domains.

 

Yes, all courses are designed to be self-paced. Learn when it fits your schedule.

Each course includes prerequisites if needed. Many Standard Courses are beginner-friendly.

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