Master
Computer Vision
With Industry Experts
Advanced Computer Vision specialization designed for AI professionals, covering state-of-the-art computer vision techniques with hands-on projects and practical implementation, leading to the Global Computer Vision Expert Certification (Advanced Level) issued by IABAC®.
- 3+ Live Projects
- Skill AI Certificate
(21,693 reviews)
Choose Your Learning Path
Blended Learning
Self Learning + Live Mentoring
₹14,005
- Self Learning + Live Mentoring
- DMC Certification
- 1 Year Access To Elearning
- 10 Capstone & 1 Client Project
- Job Assistance
- 24*7 Leaner assistance and support
Live Virtual
₹35,005
- 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
Classroom
₹60,432
- NASSCOM® Certification
- 6-Month | 200 Learning Hours
- 32-Hour Classroom Sessions
- 10 Capstone & 1 Client Project
- Cloud Lab Access
- Internship + Job Assistance
Tools & Technologies You'll Master
TensorFlow
PyTorch
Keras
SciPy
NLTK
NumPy
Pandas
Scikit Learn
Matplotlib
Seaborn
Why Skill AI Institute?
Expert Trainers
Ph.ds And Industry Experts
Elite faculty from prestigious
Universities with deep research
And coaching expertise
Career Guidance
Expert Counselors
Personalized counselling for career Enhancement in managerial roles
Specialized Syllabus
Specialized Syllabus For Managers
Focused on data science for decision making, Managing data science projects with essential technical overview
5 Case Studies
Practical Decision-making Cases
Techniques for scenarios with certainty, Low uncertainty and high certainty from Decision tree to monte carlo simulation
Flexible Financing Options
We’re dedicated to making our programs accessible. Pay in easy installments at 0% interest with no hidden costs.
EMI Available
Bajaj Finserv & ShopSe
Admission Closing
31st December 2026
Course Syllabus
Module 1: Introduction to Artificial Intelligence (AI)
• Evolution of Human Intelligence
• What is Artificial Intelligence?
• History of Artificial Intelligence
• Why Artificial Intelligence now?
• AI Terminologies
• Areas of Artificial Intelligence
• AI vs Data Science vs Machine Learning
Module 2: AI Data Strategy
• Foundation of AI Data
• Data Lake
• Four Stages of Building and Integrating Data Lakes within Technology Architectures
Module 3: AI Ethics, Issues and Concerns
• Issues and Concerns around AI
• AI and Ethical Concerns
• AI and Bias
• AI: Ethics, Bias, and Trust
Module 4: AI Challenges, Usecases and Adoption
• Challenges of AI Implementation
• Pitfalls and Lessons from the Industry
• Usecases from top AI Implementation
• Future with AI
• The Journey for adopting AI successfully
Module 5: Tensorflow Introduction
• Introduction to Tensorflow 2.X
• Tensor + Flow = Tensorflow
• Components and Basis Vectors
• Sequential and Functional APIs
Module 6: Tensorflow Basic Concepts
• Creating a Tensor
• Tensor Rank /Degree
• Shape of a Tensor
• Create Flow for Tensor Operation
• Usability-Related Changes
• Performance-Related Changes
Module 7: Installation and Basic Operations in Tf 2.X
• Tensorflow 2.X Installation and Setup
• Anaconda Distribution Installation
• Colab – Free Powerful Lab from Google
• Databricks
• Tensorflow V1.X Vs Tensorflow V2.X
• Tensorflow Architecture
• Tf 2.0 Basic Syntax
Module 8: Tf 2.0 Eager Mode
• Tensorflow Graphs
• Variables and Placeholders
• Operations and Control Statements
• Tf 2.0 Eager Execution Mode
• Tf 2.0 Autograph Tf.Function
• Application of Tensorflow Platform
Module 9: Tensorflow 2.X – Keras
• Keras Package Introduction
• Inbuilt Keras in Tensorflow2.X
• Using Keras Modules for Nn Modelling
Module 10: Structure of Neural Networks
• Neural Networks – Inspiration from the Human Brain
• Introduction to Perceptron
• Binary Classification Using Perceptron
• Perceptrons – Training
• Multiclass Classification using Perceptrons
Module 11: Neural Network - Core Concepts
• Inputs and Outputs of a Neural Network
• Parameters and Hyperparameters of Neural Networks
• Activation Functions
• Flow of Information in Neural Networks – Between 2 Layers
• Learning the Dimensions Weight Matrices
Module 12: Feed Forward Algorithm
• Feedforward Algorithm
• Vectorized Feedforward Implementation
• Understanding Vectorized Feedforward Implementation
• What does training a Network mean?
• Complexity of the Loss Function
• Comprehension – Training a Neural Network
Module 13: Backpropagation
• Sigmoid Backpropagation
• Batch in Backpropagation
• Training in Batches
• Regularization
Module 14: Building Neural Network from Scratch using Numpy
• Imports and Setups
• Defining Network Variables
• Creating Feed Forward Module
• Creating Back Propagation Module
• Integrating all Modules for Complete Neural Network
Module 15: Convolutional Neural Networks (CNNs) Introduction
• Introduction To CNNs
• Image Processing Basics
• Understanding Mammals Eye Perception
• Understanding Convolutions
• Stride and Padding
• Important Formulas
• Weights of a CNN
• Feature Maps
• Pooling
Module 16: CNNs with KERAS – Tf 2.X(CNNs) Introduction
• Building CNNs In Keras – Mnist
• Comprehension – Vgg16 Architecture
• Cifar-10 Classification with Python
• Overview of CNN Architectures
• Alexnet and Vggnet
• Googlenet
Module 17: Transfer Learning in CNN
• Introduction to Transfer Learning
• Use Cases of Transfer Learning
• Transfer Learning with Pre-Trained CNNs
• Practical Implementation of Transfer Learning
• Transfer Learning in Python
Module 18: Style Transfer
• Introduction to Style Transfer
• Style Loss and the Gram Matrix
• Loss Function
• Style Transfer Notebook
Module 19: Flowers Dataset with Tf 2.X
• Examining the Flowers Dataset
• Data Preprocessing: Shape, Size and Form
• Data Preprocessing: Normalisation
• Data Preprocessing: Augmentation
Module 20: Resnet Modeling
• Resnet: Original Architecture and Improvements
• Building the Network
• Ablation Experiments
• Hyperparameter Tuning
• Training and Evaluating the Model
Module 21: Examing X-Ray with CNN Model
• Examining X-Ray Images
• Cxr Data Preprocessing – Augmentation
• Cxr: Network Building
TESTIMONIALS
What Our Students Have To Say
Because of this, I am attracting many companies for job interviews on LinkedIn.
Truly impressed with their support and guidance.
Because of this, I am attracting many companies for job interviews on LinkedIn.
Truly impressed with their support and guidance.