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Learning to Master OpenCV 3 in Python

Best Seller 102 Lectures 10h 7m
Prepare for your Microsoft examination with our training course. The No_code course contains a complete batch of videos that will provide you with profound and thorough knowledge related to Microsoft certification exam. Pass the Microsoft No_code test with flying colors.
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Curriculum For This Course
  • 1. What are Images? 2m
  • 2. How are Images Formed? 3m
  • 3. Storing Images on Computers 5m
  • 4. Getting Started with OpenCV - A Brief OpenCV Intro 9m
  • 5. Grayscaling - Converting Color Images To Shades of Gray 2m
  • 6. Understanding Color Spaces - The Many Ways Color Images Are Stored Digitally 12m
  • 7. Histogram representation of Images - Visualizing the Components of Images 5m
  • 8. Creating Images & Drawing on Images - Make Squares, Circles, Polygons & Add Text 4m
  • 1. Transformations, Affine And Non-Affine - The Many Ways We Can Change Images 2m
  • 2. Image Translations - Moving Images Up, Down 3m
  • 3. Rotations - How To Spin Your Image Around And Do Horizontal Flipping 3m
  • 4. Scaling, Re-sizing and Interpolations - Understand How Re-Sizing Affects Quality 4m
  • 5. Image Pyramids - Another Way of Re-Sizing 2m
  • 6. Cropping - Cut Out The Image The Regions You Want or Don't Want 3m
  • 7. Arithmetic Operations - Brightening and Darkening Images 4m
  • 8. Bitwise Operations - How Image Masking Works 4m
  • 9. 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
  • 1. Segmentation and Contours - Extract Defined Shapes In Your Image 11m
  • 2. Sorting Contours - Sort Those Shapes By Size 13m
  • 3. Approximating Contours & Finding Their Convex Hull - Clean Up Messy Contours 6m
  • 4. Matching Contour Shapes - Match Shapes In Images Even When Distorted 5m
  • 5. Mini Project 2 - Identify Shapes (Square, Rectangle, Circle, Triangle & Stars) 5m
  • 6. Line Detection - Detect Straight Lines E.g 6m
  • 7. Blob Detection - Detect The Center of Flowers 3m
  • 8. Mini Project 3 - Counting Circles and Ellipses 6m
  • 1. Object Detection Overview 3m
  • 2. Mini Project # 4 - Finding Waldo (Quickly Find A Specific Pattern In An Image) 3m
  • 3. Feature Description Theory - How We Digitally Represent Objects 5m
  • 4. Finding Corners - Why Corners In Images Are Important to Object Detection 7m
  • 5. SIFT, SURF, FAST, BRIEF & ORB - Learn The Different Ways To Get Image Features 10m
  • 6. Mini Project 5 - Object Detection - Detect A Specific Object Using Your Webcam 15m
  • 7. Histogram of Oriented Gradients - Another Novel Way Of Representing Images 8m
  • 1. HAAR Cascade Classifiers - Learn How Classifiers Work And Why They're Amazing 5m
  • 2. Face and Eye Detection - Detect Human Faces and Eyes In Any Image 11m
  • 3. Mini Project 6 - Car and Pedestrian Detection in Videos 7m
  • 1. Face Analysis and Filtering - Identify Face Outline, Lips, Eyes Even Eyebrows 11m
  • 2. Merging Faces (Face Swaps) - Combine Two Faces For Fun & Sometimes Scary Results 9m
  • 3. Mini Project 7 - Live Face Swapper (like MSQRD & Snapchat filters!!!) 6m
  • 4. Mini Project 8 - Yawn Detector and Counter 9m
  • 1. Machine Learning Overview - What Is It & Why It's Important to Computer Vision 9m
  • 2. Mini Project 9 - Handwritten Digit Classification 20m
  • 3. Mini Project # 10 - Facial Recognition - Make Your Computer Recognize You 12m
  • 1. Filtering by Color 6m
  • 2. Background Subtraction and Foreground Subtraction 7m
  • 3. Using Meanshift for Object Tracking 5m
  • 4. Using CAMshift for Object Tracking 4m
  • 5. Optical Flow - Track Moving Objects In Videos 7m
  • 6. Mini Project # 11 - Ball Tracking 5m
  • 1. Mini Project # 12 - Photo-Restoration 7m
  • 1. Course Summary and how to become an Expert 3m
  • 2. Latest Advances, 12 Startup Ideas & Implementing Computer VIsion inm Mobile Apps 7m
  • 1. Setup your Deep Learning Virtual Machine 10m
  • 2. Intro to Handwritten Digit Classification (MNIST) 6m
  • 3. Intro to Multiple Image Classification (CIFAR10) 3m
  • 1. Neural Networks Chapter Overview 2m
  • 2. Machine Learning Overview 8m
  • 3. Neural Networks Explained 4m
  • 4. Forward Propagation 9m
  • 5. Activation Functions 9m
  • 6. Training Part 1 – Loss Functions 9m
  • 7. Training Part 2 – Backpropagation and Gradient Descent 10m
  • 8. Backpropagation & Learning Rates – A Worked Example 14m
  • 9. 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
  • 1. Convolutional Neural Networks Chapter Overview 1m
  • 2. Introduction to Convolutional Neural Networks (CNNs) 5m
  • 3. Convolutions & Image Features 13m
  • 4. Depth, Stride and Padding 7m
  • 5. ReLU 2m
  • 6. Pooling 5m
  • 7. The Fully Connected Layer 2m
  • 8. Training CNNs 3m
  • 9. Designing Your Own CNN 4m
  • 1. Introduction to Keras & Tensorflow 1m
  • 2. Building a CNN in Keras 12m
  • 3. Building a Handwriting Recognition CNN 2m
  • 4. Loading Our Data 6m
  • 5. Getting our data in ‘Shape’ 4m
  • 6. Hot One Encoding 3m
  • 7. Building & Compiling Our Model 4m
  • 8. Training Our Classifier 5m
  • 9. 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
  • 1. Data Augmentation Chapter Overview 1m
  • 2. Splitting Data into Test and Training Datasets 10m
  • 3. Train a Cats vs 4m
  • 4. Boosting Accuracy with Data Augmentation 5m
  • 5. Types of Data Augmentation 5m

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