Diploma in Advanced Python & Computer Vision

Duration: 6 Months
Total Hours: 450
Lecture Hours: 100
Mode: Online
Award: Diploma Certificate from UGC recognized University
Entry Requirement: Basic computer knowledge / No prior programming experience required

Course Overview

The Diploma in Advanced Python and Computer Vision course teaches Python programming from
basics to advanced topics, data handling with NumPy and Pandas, and image processing using
OpenCV.
It covers machine learning, deep learning, real-time computer vision, and deployment techniques,
enabling students to build practical AI-driven computer vision applications.

Course Content Summary

  • Python Basics
    Learn the foundation: variables, data types, loops, conditions, functions, and basic data
    structures like lists, dictionaries, sets, and tuples. The goal is to write simple Python
    programs confidently.

  • Advanced Python
    Dive deeper into object-oriented programming, decorators, generators, advanced data
    structures, error handling, and concurrency using threads and async. The goal is to write
    professional, clean, and efficient Python code.

  • Data Handling and Scientific Computing
    Work with NumPy for arrays and mathematical operations, Pandas for data cleaning,
    analysis, and time-series handling, and visualize data with Matplotlib. The goal is to
    handle and analyze large datasets efficiently.

  • Computer Vision Foundations
    Understand images, including pixels, colors, transformations, and basic processing like
    blur, sharpen, thresholding, and morphology. The goal is to learn how images work and
    how to manipulate them.

  • OpenCV and Practical Image Processing
    Learn OpenCV for real-world tasks like image input/output, drawing, masking, edge
    detection, contours, and object tracking. The goal is to build basic computer vision
    applications.

  • Advanced and Real-Time Computer Vision
    Focus on object detection, segmentation, facial recognition, and pose estimation. Work
    on real-time tracking and live video analysis using OpenCV DNN and Deep SORT. The
    goal is to build real-time AI systems for practical use.

  • Deployment and Projects
    Build complete applications with GUI (Tkinter or PyQt), APIs (Flask or FastAPI),
    Docker, and cloud deployment. Final projects include face recognition, vehicle detection,
    surveillance systems, and medical image analysis. The goal is to turn your computer
    vision models into real-world applications.

Course Lecturer

Chulendra Wibhawashakthi

BSc (Hons). Engineer -University of Ruhuna

(IESL, ECSL)

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