Certification training

Learning to Master OpenCV 3 in Python

Prepare for your exam with a structured video course built for focused study, topic review, and repeatable progress before exam day.

102Lectures
10h 7mDuration
Vendor
Learning to Master OpenCV 3 in Python course cover Best seller
certification training 102 lessons arranged in a guided course path
Course overview

Study with a clear path.

Move through focused sections, review lecture timing, and return directly to the objectives that need more work.

Sequenced lessons

Follow the course in order or jump directly to the section you need to revisit.

Video-first prep

Use concise lecture blocks for technical review before moving into exam practice.

Fast access

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Curriculum

Course sections.

16 sections, 102 lessons, and lecture-level duration details.

01 Introduction 2m
02 Introduction to Computer Vision and OpenCV 3m
03 About this course 5m
04 Recomended - Setup your OpenCV4.0.1 Virtual Machine 6m
05 Set up course materials (DOWNLOAD LINK BELOW) - Not needed if using the new VM 2m

01 What are Images? 2m
02 How are Images Formed? 3m
03 Storing Images on Computers 5m
04 Getting Started with OpenCV - A Brief OpenCV Intro 9m
05 Grayscaling - Converting Color Images To Shades of Gray 2m
06 Understanding Color Spaces - The Many Ways Color Images Are Stored Digitally 12m
07 Histogram representation of Images - Visualizing the Components of Images 5m
08 Creating Images & Drawing on Images - Make Squares, Circles, Polygons & Add Text 4m

01 Transformations, Affine And Non-Affine - The Many Ways We Can Change Images 2m
02 Image Translations - Moving Images Up, Down 3m
03 Rotations - How To Spin Your Image Around And Do Horizontal Flipping 3m
04 Scaling, Re-sizing and Interpolations - Understand How Re-Sizing Affects Quality 4m
05 Image Pyramids - Another Way of Re-Sizing 2m
06 Cropping - Cut Out The Image The Regions You Want or Don't Want 3m
07 Arithmetic Operations - Brightening and Darkening Images 4m
08 Bitwise Operations - How Image Masking Works 4m
09 Blurring - The Many Ways We Can Blur Images & Why It's Important 7m
10 Sharpening - Reverse Your Images Blurs 2m
11 Thresholding (Binarization) - Making Certain Images Areas Black or White 9m
12 Dilation, Erosion, Opening/Closing - Importance of Thickening/Thinning Lines 5m
13 Perspective & Affine Transforms - Take An Off Angle Shot & Make It Look Top Down 4m
14 Mini Project 1 - Live Sketch App - Turn your Webcam Feed Into A Pencil Drawing 5m

01 Segmentation and Contours - Extract Defined Shapes In Your Image 11m
02 Sorting Contours - Sort Those Shapes By Size 13m
03 Approximating Contours & Finding Their Convex Hull - Clean Up Messy Contours 6m
04 Matching Contour Shapes - Match Shapes In Images Even When Distorted 5m
05 Mini Project 2 - Identify Shapes (Square, Rectangle, Circle, Triangle & Stars) 5m
06 Line Detection - Detect Straight Lines E.g 6m
07 Blob Detection - Detect The Center of Flowers 3m
08 Mini Project 3 - Counting Circles and Ellipses 6m

01 Object Detection Overview 3m
02 Mini Project # 4 - Finding Waldo (Quickly Find A Specific Pattern In An Image) 3m
03 Feature Description Theory - How We Digitally Represent Objects 5m
04 Finding Corners - Why Corners In Images Are Important to Object Detection 7m
05 SIFT, SURF, FAST, BRIEF & ORB - Learn The Different Ways To Get Image Features 10m
06 Mini Project 5 - Object Detection - Detect A Specific Object Using Your Webcam 15m
07 Histogram of Oriented Gradients - Another Novel Way Of Representing Images 8m

01 HAAR Cascade Classifiers - Learn How Classifiers Work And Why They're Amazing 5m
02 Face and Eye Detection - Detect Human Faces and Eyes In Any Image 11m
03 Mini Project 6 - Car and Pedestrian Detection in Videos 7m

01 Face Analysis and Filtering - Identify Face Outline, Lips, Eyes Even Eyebrows 11m
02 Merging Faces (Face Swaps) - Combine Two Faces For Fun & Sometimes Scary Results 9m
03 Mini Project 7 - Live Face Swapper (like MSQRD & Snapchat filters!!!) 6m
04 Mini Project 8 - Yawn Detector and Counter 9m

01 Machine Learning Overview - What Is It & Why It's Important to Computer Vision 9m
02 Mini Project 9 - Handwritten Digit Classification 20m
03 Mini Project # 10 - Facial Recognition - Make Your Computer Recognize You 12m

01 Filtering by Color 6m
02 Background Subtraction and Foreground Subtraction 7m
03 Using Meanshift for Object Tracking 5m
04 Using CAMshift for Object Tracking 4m
05 Optical Flow - Track Moving Objects In Videos 7m
06 Mini Project # 11 - Ball Tracking 5m

01 Mini Project # 12 - Photo-Restoration 7m

01 Course Summary and how to become an Expert 3m
02 Latest Advances, 12 Startup Ideas & Implementing Computer VIsion inm Mobile Apps 7m

01 Setup your Deep Learning Virtual Machine 10m
02 Intro to Handwritten Digit Classification (MNIST) 6m
03 Intro to Multiple Image Classification (CIFAR10) 3m

01 Neural Networks Chapter Overview 2m
02 Machine Learning Overview 8m
03 Neural Networks Explained 4m
04 Forward Propagation 9m
05 Activation Functions 9m
06 Training Part 1 – Loss Functions 9m
07 Training Part 2 – Backpropagation and Gradient Descent 10m
08 Backpropagation & Learning Rates – A Worked Example 14m
09 Regularization, Overfitting, Generalization and Test Datasets 15m
10 Epochs, Iterations and Batch Sizes 4m
11 Measuring Performance and the Confusion Matrix 7m
12 Review and Best Practices 4m

01 Convolutional Neural Networks Chapter Overview 1m
02 Introduction to Convolutional Neural Networks (CNNs) 5m
03 Convolutions & Image Features 13m
04 Depth, Stride and Padding 7m
05 ReLU 2m
06 Pooling 5m
07 The Fully Connected Layer 2m
08 Training CNNs 3m
09 Designing Your Own CNN 4m

01 Introduction to Keras & Tensorflow 1m
02 Building a CNN in Keras 12m
03 Building a Handwriting Recognition CNN 2m
04 Loading Our Data 6m
05 Getting our data in ‘Shape’ 4m
06 Hot One Encoding 3m
07 Building & Compiling Our Model 4m
08 Training Our Classifier 5m
09 Plotting Loss and Accuracy Charts 3m
10 Saving and Loading Your Model 3m
11 Displaying Your Model Visually 3m
12 Building a Simple Image Classifier using CIFAR10 7m

01 Data Augmentation Chapter Overview 1m
02 Splitting Data into Test and Training Datasets 10m
03 Train a Cats vs 4m
04 Boosting Accuracy with Data Augmentation 5m
05 Types of Data Augmentation 5m
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