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Computer Vision using AI and Deep Learning

Name: Computer Vision using AI and Deep Learning
Rating: 5.00 (2 reviews)

Accelerate your Career

Learn AI-powered computer vision applications. Explore deep learning, image recognition, object detection, and real-world deployment with Python and OpenCV.

8 Modules

with Certifications

11:42 Hours

of Recorded Content

5.0 Ratings

by 690 Learners

English

Language

Paid Course

Get this Course @ ₹1,499

Name

Email ID

Phone Number

690 enrolled in this course

Course Specifications

Sections

Lessons

Capacity

Unlimited

Duration

11:42 Hours

Students

690

Access Duration

90 Days

Created Date

2 Jun 2026

Updated Date

28 Jun 2026

Computer Vision using AI and Deep Learning

Computer Vision has become one of the fastest-growing domains in Artificial Intelligence, enabling machines to understand and interpret visual information. This explorer-level course introduces learners to practical computer vision concepts alongside modern deep learning techniques used for image classification and object detection. Through hands-on implementation, participants will develop applications such as vehicle detection systems, face recognition, smart attendance, handwritten text recognition, OCR pipelines, and YOLO-based object detection models. The curriculum combines traditional OpenCV techniques with neural network-based approaches to provide a balanced understanding of intelligent vision systems widely adopted across industry sectors.

MASTERING THE DATA SCIENCE LIFECYCLE

Comprehensive Syllabus Outline

A comprehensive curriculum designed to take you from beginner to professional.

M1

Module 1 — Computer Vision Fundamentals and Image Processing

Introduction to Computer Vision Concepts, Applications and Industry Use Cases

Transfer Learning using Pretrained CNN Models

M2

Module 2 — Object Detection and Tracking Applications

Vehicle Detection, Counting and Traffic Monitoring using OpenCV

Colorizing Grayscale Images using Computer Vision and Deep Learning Techniques

M3

Module 3 — Face Analytics and Recognition Systems

Face and Eye Detection using Haar Cascade Classifiers in OpenCV

Building a Face Recognition System using Computer Vision Techniques

Developing an Automated Face Mask Detection System using AI

Facial Emotion Recognition using Computer Vision and Deep Learning

M4

Module 4 — Intelligent Computer Vision Applications

Designing a Smart Attendance System using Face Recognition

Generating Descriptive Image Captions using Deep Learning Models

Handwritten Text Recognition using Computer Vision Techniques

Optical Character Recognition and Automated Text Extraction using OCR

M5

Module 5 — Deep Learning Fundamentals and Neural Networks

Introduction to Deep Learning Concepts and Neural Network Architectures

Designing and Training Your First Artificial Neural Network

M6

Module 6 — Deep Learning for Image Classification

Image Classification using Convolutional Neural Networks (CNNs)

Leaf Disease Detection using Deep Learning-Based Image Classification

M7

Module 7 — Object Detection using Deep Learning

Real-Time Object Detection using YOLO and OpenCV Frameworks

Module 7 — Object Detection using Deep Learning

M8

Module 8 — Capstone Project

Driver Drowsiness Detection System using Computer Vision and Deep Learning

Capstone Project

AI Vision Systems Portfolio

Build end-to-end intelligent vision applications demonstrating practical implementation of computer vision and deep learning techniques used in modern AI solutions.