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Computer Vision and Deep Learning with OpenCV and Python - Build 15 Projects

Learn a new skill to snag a new job now!

What's Inside

Course Curriculum

Course Overview - Rendered

01 Course Overview - Opencv - 4:51
Preview
02 What You'll Need - 2:38
Start
Course Overview - Source File
Start

Introduction to Python (Prerequisite)

00. Intro To Course And Python - 9:57
Start
01. Variables - 19:19
Start
02. Type Conversion Examples - 10:06
Start
03. Operators - 28:54
Start
04. Collections - 8:25
Start
05. List Examples - 19:41
Start
06. Tuples Examples - 8:36
Start
07. Dictionaries Examples - 14:26
Start
08. Ranges Examples - 8:32
Start
09. Conditionals - 6:43
Start
10. If Statement Examples - 21:32
Start
11. Loops - 29:42
Start
12. Functions - 17:01
Start
13. Parameters And Return Values Examples - 13:54
Start
14. Classes And Objects - 34:11
Start
15. Inheritance Examples - 17:29
Start
16. Static Members Examples - 11:05
Start
17. Summary And Outro - 4:08
Start
Source Code
Start

Analyze Images with OpenCV

01 Detect Edges In An Image - 8:19
Start
02 Detect Contours In An Image - 11:23
Start
03 Detect Corners In An Image - 9:37
Start
Analyze Images
Start

Restore Images with Computational Photography

01 Restore A Damaged Image - 18:57
Start
Computational Photography
Start

Detect objects in images

01 Detect Objects In An Image With Masking - 15:39
Start
04 Find Object In Image With Template Matching - 12:27
Start
03 Extract Foreground In An Image - 18:30
Start
02 Detect Faces In Images - 11:31
Start

Machine learning and neural networks introduction

01 What Is Machine Learning - 5:26
Start
02 What Is Deep Learning - 7:42
Start
03 What Is A Neural Network - 8:47
Start
04 What Is Ml-Agents - 5:16
Start
Source Files
Start

Read text in an image with OCR and Tesseract

01 Extract Text From An Image With Tesseract - 13:31
Start
02 Improve Accuracy With Thresholding - 8:10
Start
03 Change Perspective Of An Image With Foreign Text - 15:30
Start
04 Extract Foreign Language Text From An Image - 8:03
Start

Build your first neural network with OpenCV

01 Generate Data - 7:09
Start
02 Build An Artificial Neural Network - 9:16
Start
03 Visualize Model Results - 14:30
Start
SourceFiles
Start

Object detection with OpenCV Deep Learning

01 Load Yolo Dnn Model - 3:18
Start
02 Build A Neural Network With Opencv - 7:44
Start
03 Print Out Detected Objects - 6:44
Start
04 Outline Objects In The Original Image - 21:57
Start
Source Files
Start

Outline objects in a video

01 Outline Objects In A Video - 10:55
Start
03 Save New Frames As A Video - 5:04
Start
Outline objects in a video
Start
02 Draw Contours On Video - 16:30
Start

Detect faces in video

01 Load A Video From Drive - 7:39
Start
02 Detect Faces In Video - 10:32
Start
03 Detect Eyes In Video - 6:40
Start
04 Save New Frames As A Video - 7:40
Start
Detect faces in video
Start

Track a color in videos

01 Track Color In A Video - 20:06
Start
02 Save New Frames As A Video - 7:14
Start
Track a color in videos
Start

Detect lanes for autonomous vehicle computer vision

01 Load A Driving Dash Cam Video - 4:05
Start
02 Process Each Video Frame - 14:54
Start
03 Outline Lanes Detected - 12:21
Start
04 Save New Frames As A Video - 13:52
Start
Source Files
Start

Build a motion alert video monitoring system

01 Load A Video From Drive - 5:28
Start
02 Detect Objects In A Video With Contours - 10:05
Start
03 Detect When Motion Begins And Ends - 15:17
Start
04 Record Each Time Motion Begins - 16:36
Start
Source Files
Start

Detect emotion in a video

Detect Emotion In A Video - 12:11
Start
Source Files
Start

Swap faces with machine learning

01 Load Images From The Web Into Colab - 3:00
Start
02 Get Facial Landmarks From Image - 11:55
Start
03 Build A Matrix From Landmark Points - 10:08
Start
04 Draw A Mask Over Facial Landmarks - 7:07
Start
05 Build A Warped Mask - 4:09
Start
06 Combine Face Masks - 8:15
Start
Source Files
Start

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Computer Vision and Deep Learning with OpenCV and Python - Build 15 Projects Coupon Discount

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Deep Learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data.

Deep learning is now used in most areas of technology, business, and entertainment. And it's becoming more important every year.

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11695+ Students

81 Lectures

13+ Hours of Video

Lifetime Access

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Instructor Rating

Alexandra Kropova

Alexandra Kropova is a software developer specializing in Java and JavaScript, with extensive experience in full-stack web development and app development. She has helped produce courses for Mammoth Interactive INC. since 2016.

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Computer Vision and Deep Learning with OpenCV and Python - Build 15 Projects

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What's Inside

Course Curriculum

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Computer Vision and Deep Learning with OpenCV and Python - Build 15 Projects Coupon Discount

Alexandra Kropova