Certified Computer

Vision Expert

Master computer vision to be ahead
of the competition today!

5 Days / 40 Hours / 13 Modules
+ 3 month coaching + 1 year eLearning access

Get in Touch With Us
Certified Computer

Vision Expert

Start your data science journey
with Python and power up
your career today!

5 Days / 40 Hours / 13 Modules
+ 3 month coaching + 1 year eLearning access

Master computer vision to be ahead of the competition today!

Senior Executives Certification in Artificial Intelligence imparts high level knowledge on AI, challenges and limitations, the essentials for strategic decision making. The course covers economics of AI, AI data strategy, industry use cases in practice etc.,
Key Learning Outcomes

Upon completion, participants should be able to demonstrate each of the following outcome:-

  • Gain knowledge in Image Representation And Analysis
  • Understand Image Segmentation
  • Display proficiency in Advanced CNN Architectures
  • Be able to perform object motion and tracking
IABAC Exam Information
Course Modules Covered in the Certified Computer Vision Expert program
Module 1 - Introduction To Computer Vision

Introduction To Computer Vision

  1. Learn where computer vision techniques are used in industry
  2. Prepare for the course ahead with a detailed topic overview
  3. Start programming your own applications
Module 2 - Image Representation And Analysis

Image Representation And Analysis

  1. See how images are represented numerically
  2. Implement Image Processing techniques like colour and Geometric Transforms
Module 3 - Convolutional Neural Network

Convolutional Neural Network

  1. Learn about the layers of a Deep Convolutional Neural Network
  2. Convolutional, Max Pooling, and Fully Connected Layers
  3. Build a CNN-based Image Classifier in PyTorch
  4. Learn about Layer Activation and Feature Visualization techniques
Module 4 - Features And Object Recognition

Features And Object Recognition

  1. Learn why distinguishing features are important in pattern and object recognition tasks
  2. Write code to extract information about an object’s colour and shape
  3. Use features to identify areas on a face and to recognize the shape of a car or pedestrian on a road
Module 5 - Image Segmentation

Image Segmentation

  1. Implement K-Means Clustering to break an image up into parts
  2. Find the contours and edges of multiple objects in an image
  3. Learn about background subtraction for video
Module 6 - Project : Automatic Image Captioning

Project : Automatic Image Captioning

  1. Combine CNN and RNN knowledge to build a deep learning model that produces captions given an input image.
    Image captioning requires that you create a complex deep learning model with two components: a CNN that transforms an input image into a set of features, and an RNN that turns those features into rich, descriptive language.
    In this project, you will implement these cutting-edge deep learning architectures.
Module 7 - Advanced CNN Architectures

Advanced CNN Architectures

  1. Learn about advance CNN architectures
  2. See how region-based CNN’s, like Faster R-CNN, have allowed for fast, localized object recognition in images
  3. Work with a YOLO/single shot object detection system
Module 8 - Recurrent Neural Networks

Recurrent Neural Networks

  1. Learn how Recurrent Neural Networks learn from ordered sequences of data
  2. Implement an RNN for sequential text generation
  3. Explore how memory can be incorporated into a Deep Learning model
  4. Understand where RNN’s are used in deep learning applications
Module 9 - Attention Mechanisms

Attention Mechanisms

  1. Learn how attention allows models to focus on a specific piece of input data
  2. Understand where attention is useful in Natural Language and Computer Vision applications
Module 10 - Image Captioning

Image Captioning

  1. Learn how to combine CNNs and RNNs to build a complex captioning model
  2. Implement an LSTM for caption generation
  3. Train a model to predict captions and understand a visual scene
Module 11 - Project: Landmark Detection And Tracking

Project: Landmark Detection And Tracking

  1. Use feature detection and key point descriptors to build a map of the environment with SLAM (Simultaneous Localization and Mapping). Implement a robust method for tracking an object over time, using elements of Probability, Motion Models, and Linear Algebra. This project tests your knowledge of localization techniques that are widely used in autonomous vehicle navigation
Module 12 - Object Motion and Tracking

Object Motion and Tracking

  1. Learn how to programmatically track a single point over time
  2. Understand motion models that define object movement over time
  3. Learn how to analyse videos as sequences of individual image frames
Module 13 - Optical Flow And Feature Matching

Optical Flow And Feature Matching

  1. Implement a method for tracking a set of unique features over time
  2. Learn how to match features from one image frame to another
  3. Track a moving car using optical flow
Our Training Methodology
Program Key Highlights

40 hours of Remote Online Learning
Additional Coaching Hours
Live Hands-on Projects
Certified by International Body
Mentorship with Industry Experts
Designed for Beginners & Professionals
Program Benefits

Get Professionally Certified

Upon successfully completing this program, participants will be awarded the Certified Computer Vision Expert Certification by International Association of Business Analytics Certification (IABAC).
This award is a validation to the efforts taken to master the domain expertise that will set you apart from your competition.
Be a part of the global network of data science professionals and join the community across sectors.
Get in Touch With Us Today!

This training program is suitable for anyone who intends to enter into the field of Artificial Intelligence. This program is being conducted in Malaysia and can be joined by anyone, anywhere in the world remotely.
Program Fee

MYR 6800 per pax.
Funding price MYR 3700 per pax .

Funding Schemes for Companies who are claiming from their HRDF levy or from the MDEC MyWiT scheme.

Limited scholarships available for early self applying individual applicants.

Find out how you can qualify for a scholarship.

Enquire NOW on the various funding options available.

One-time fee. One year access to course materials and resources.

Please fill in the form and a Program Advisor will reach out to you. You can also reach out to us at info@thulija.com or +60123661502
Contact us on Whatsapp for more enquiries