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Computer Vision professionals are the most coveted specialization in the AI domain.
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Learning state-of-art techniques of computer vision with hands-on projects.
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Global Computer Vision Expert Certification - advanced level issued by IABAC®
CERTIFIED COMPUTER VISION EXPERT COURSE
4.9 (21,693) reviews
Accredited by
3+ Live
Projects
Skill AI
Certificate
CERTIFIED COMPUTER VISION EXPERT CERTIFICATION AUTHORITIES
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Live Virtual
Instructor Led Live Online
₹55,221
NASSCOM® Certification
6-Month / 200 Learning Hours
80-Hour Live Online Training
10 Capstone & 1 Client Project
365 Days Flexi Pass + Cloud Lab
Internship + Job Assistance
CERTIFIED COMPUTER VISION EXPERT COURSE FEE
Blended Learning
Self Learning + Live Mentoring
₹32,505
Self Learning + Live Mentoring
NASSCOM® Certification
1 Year Access To Elearning
10 Capstone & 1 Client Project
Job Assistance
24*7 Leaner assistance and support
Financing Options
We are dedicated to making our programs accessible. We are committed to helping you find a way to budget for this program and offer a variety of financing options to make it more economical.
Pay In Installments, as low as
We have partnered with the following financing companies to provide competitive finance options at as low as
0% interest rates with no hidden cost.
Admission Closes On : 31st December 2026
WHY SKILL AI INSTITUTE FOR COMPUTER VISION EXPERT COURSE
Expert Trainers
PH.Ds AND INDUSTRY EXPERTS
ELITE FACULTY FROM PRESTIGIOUS
UNIVERSITIES WITH DEEP RESEARCH
AND COACHING EXPERTISE
Specialized Syllabus
SPECIALIZED SYLLABUS FOR MANAGERS
PERSONALIZED COUNSELLING FOR CAREER
ENHANCEMENT IN MANAGERIAL ROLES
FOCUSED ON DATA SCIENCE FOR DECISION MAKING,
MANAGING DATA SCIENCE PROJECTS WITH ESSENTIAL TECHNICAL OVERVIEW
Career Guidance
EXPERT COUNSELORS
TECHNIQUES FOR SCENARIOS WITH CERTAINTY,
LOW UNCERTAINTY AND HIGH CERTAINTY FROM
DECISION TREE TO MONTE CARLO SIMULATION
5 Case Studies
PRACTICAL DECISION-MAKING CASES
SYLLABUS OF CERTIFIED COMPUTER VISION EXPERT COURSE
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Evolution of Human Intelligence
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What is Artificial Intelligence?
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History of Artificial Intelligence
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Why Artificial Intelligence now?
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AI Terminologies
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Areas of Artificial Intelligence
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AI vs Data Science vs Machine Learning
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Foundation of AI Data
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Data Lake
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Four Stages of Building and Integrating Data Lakes within Technology Architectures
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Issues and Concerns around AI
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AI and Ethical Concerns
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AI and Bias
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AI: Ethics, Bias, and Trust
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Challenges of AI Implementation
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Pitfalls and Lessons from the Industry
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Usecases from top AI Implementation
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Future with AI
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The Journey for adopting AI successfully
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Introduction to Tensorflow 2.X
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Tensor + Flow = Tensorflow
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Components and Basis Vectors
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Sequential and Functional APIs
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Creating a Tensor
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Tensor Rank /Degree
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Shape of a Tensor
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Create Flow for Tensor Operation
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Usability-Related Changes
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Performance-Related Changes
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Tensorflow 2.X Installation and Setup
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Anaconda Distribution Installation
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Colab – Free Powerful Lab from Google
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Databricks
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Tensorflow V1.X Vs Tensorflow V2.X
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Tensorflow Architecture
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Tf 2.0 Basic Syntax
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Tensorflow Graphs
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Variables and Placeholders
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Operations and Control Statements
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Tf 2.0 Eager Execution Mode
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Tf 2.0 Autograph Tf.Function
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Application of Tensorflow Platform
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Keras Package Introduction
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Inbuilt Keras in Tensorflow2.X
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Using Keras Modules for Nn Modelling
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Neural Networks - Inspiration from the Human Brain
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Introduction to Perceptron
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Binary Classification Using Perceptron
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Perceptrons - Training
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Multiclass Classification using Perceptrons
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Working of a Neuron
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Inputs and Outputs of a Neural Network
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Parameters and Hyperparameters of Neural Networks
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Activation Functions
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Flow of Information in Neural Networks - Between 2 Layers
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Learning the Dimensions Weight Matrices
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Feedforward Algorithm
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Vectorized Feedforward Implementation
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Understanding Vectorized Feedforward Implementation
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What does training a Network mean?
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Complexity of the Loss Function
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Comprehension - Training a Neural Network
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Updating the Weights and Biases
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Sigmoid Backpropagation
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Batch in Backpropagation
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Training in Batches
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Regularization
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Batch Normalization
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Imports and Setups
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Defining Network Variables
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Creating Feed Forward Module
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Creating Back Propagation Module
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Integrating all Modules for Complete Neural Network
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Predictions using the Network Model
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Introduction To CNNs
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Image Processing Basics
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Understanding Mammals Eye Perception
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Understanding Convolutions
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Stride and Padding
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Important Formulas
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Weights of a CNN
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Feature Maps
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Pooling
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Putting the Components Together
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Building CNNs In Keras - Mnist
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Comprehension - Vgg16 Architecture
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Cifar-10 Classification with Python
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Overview of CNN Architectures
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Alexnet and Vggnet
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Googlenet
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Residual Net
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Introduction to Transfer Learning
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Use Cases of Transfer Learning
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Transfer Learning with Pre-Trained CNNs
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Practical Implementation of Transfer Learning
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Transfer Learning in Python
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An analysis of Deep Learning Models
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Introduction to Style Transfer
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Style Loss and the Gram Matrix
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Loss Function
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Style Transfer Notebook
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Object Detection
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Examining the Flowers Dataset
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Data Preprocessing: Shape, Size and Form
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Data Preprocessing: Normalisation
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Data Preprocessing: Augmentation
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Data Preprocessing: Practice Exercise Solutions
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Resnet: Original Architecture and Improvements
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Building the Network
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Ablation Experiments
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Hyperparameter Tuning
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Training and Evaluating the Model
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Examining X-Ray Images
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Cxr Data Preprocessing – Augmentation
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Cxr: Network Building
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