Beginner's Guide to Learn Computer Vision with Python

Beginner's Guide to Learn Computer Vision with Python, available at Free, has an average rating of 4.45, with 42 lectures, 7 quizzes, based on 20 reviews, and has 737 subscribers.

You will learn about Learn fundamentals of Computer Vision Understand state of art image and video processing Algorithms in CV Understand application of Deep Learning Models in the CV Learn to implement CV algorithms with OpenCV python library This course is ideal for individuals who are Freshers and experienced professionals interested in learning 'Computer Vision' It is particularly useful for Freshers and experienced professionals interested in learning 'Computer Vision'.

Enroll now: Beginner's Guide to Learn Computer Vision with Python

Summary

Title: Beginner's Guide to Learn Computer Vision with Python

Price: Free

Average Rating: 4.45

Number of Lectures: 42

Number of Quizzes: 7

Number of Published Lectures: 42

Number of Published Quizzes: 7

Number of Curriculum Items: 49

Number of Published Curriculum Objects: 49

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Learn fundamentals of Computer Vision
Understand state of art image and video processing Algorithms in CV
Understand application of Deep Learning Models in the CV
Learn to implement CV algorithms with OpenCV python library

Who Should Attend

Freshers and experienced professionals interested in learning 'Computer Vision'

Target Audiences

Freshers and experienced professionals interested in learning 'Computer Vision'

Learn fundamentals of Computer Vision with state of art image and video processing Algorithms.

Course Structure

Introduction:

Introduction
Real world Applications

Popular Computer Vision Techniques:

Image Segmentation
Demo – Image Segmentation
Edge Detection
Demo – Edge Detection
Feature Extraction
Demo – Feature Extraction
Application of CV techniques

Object Detection, Tracking and Classification:

Object Detection
Object Tracking
Image Classification
Demo: Image Classification
Challenges in CV

Deep Learning for Computer Vision:

What is Deep Learning?
Convolutional Neural Network (CNN)
Demo – CNN
Transfer Learning
Benefits of Deep Learning in CV

Image Recognition:

Face Detection and Recognition
Demo – Face Detection
Optical Character Recognition (OCR)
Demo – OCR

Advanced Techniques – Panorama Creation:

Image Registration
Image Stitching
Demo – Image Stitching

Motion Analysis:

Motion Analysis
Video Processing
Background Subtraction
Demo: Background Subtraction

Realtime Video Processing:

Realtime Video Processing
Demo – Object Detection
Application in Robotics

Requirements

Basics knowledge of computer programming
Familiar with python programming language and any python IDE (like PyCharm)
Windows / Linux / Mac OS X Machine with Internet

Content team

Expert: Arunkumar Krishnan
Production: Vishnu Sakthivel, Visshwa Balasubramanian

What you will learn?

Learn fundamentals of Computer Vision
Understand state of art image and video processing Algorithms in CV
Understand application of Deep Learning Models in the CV
Learn to implement CV algorithms with OpenCV python library

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Real world Applications

Lecture 3: Summary

Chapter 2: Popular Computer Vision Techniques

Lecture 1: Image Segmentation

Lecture 2: Demo – Image Segmentation

Lecture 3: Edge Detection

Lecture 4: Demo – Edge Detection

Lecture 5: Feature Extraction

Lecture 6: Demo – Feature Extraction

Lecture 7: Application of CV techniques

Lecture 8: Summary

Chapter 3: Object Detection, Tracking and Classification

Lecture 1: Object Detection

Lecture 2: Object Tracking

Lecture 3: Image Classification

Lecture 4: Demo: Image Classification

Lecture 5: Challenges in CV

Lecture 6: Summary

Chapter 4: Deep Learning in Computer Vision

Lecture 1: What is Deep Learning?

Lecture 2: Convolutional Neural Network (CNN)

Lecture 3: Demo – CNN

Lecture 4: Transfer Learning

Lecture 5: Demo – Transfer Learning

Lecture 6: Benefits of Deep Learning in CV

Lecture 7: Summary

Chapter 5: Image Recognition

Lecture 1: Face Detection and Recognition

Lecture 2: Demo – Face Detection

Lecture 3: Optical Character Recognition (OCR)

Lecture 4: Demo – OCR

Lecture 5: Summary

Chapter 6: Advanced Techniques – Panorama Creation

Lecture 1: Image Registration

Lecture 2: Image Stitching

Lecture 3: Demo – Image Stitching

Lecture 4: Summary

Chapter 7: Motion Analysis

Lecture 1: Motion Analysis

Lecture 2: Video Processing

Lecture 3: Background Subtraction

Lecture 4: Demo: Background Subtraction

Lecture 5: Summary

Chapter 8: Realtime Video Processing

Lecture 1: Realtime Video Processing

Lecture 2: Demo – Object Detection

Lecture 3: Application in Robotics

Lecture 4: Summary

Instructors

Techjedi LLP
Learn from Experts

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 3 votes
4 stars: 7 votes
5 stars: 9 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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