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Computer Vision Masters

This course is designed to cater a learner’s need to learn all about Computer Vision.Get the best online Computer Science course training from top data scientists.

Key Features

  • Total Session Hours : 60-70 hrs
  • Duration : 12 weeks
  • Efforts : 15 hrs/week
  • Live Projects/Demos : 20+
  • Certification and Job Assistance
  • Flexible Schedule
  • Lifetime free upgrade
  • 24 x 7 Lifetime Support & Access
  • Certified by top MNCs
  • Cloud computing enabled real industry projects
  • CV and Profile revamp
  • Job referrals and assistance
  • Internship opportunities with industry interface
  • Kaggle exposure and path for becoming grandmaster

Next Batch : 11th July – Almost filled up. Register for free Webinar on the course introduction here

 

 

Below is the curriculum of the course:

Module 1 : Mandatory Conceptual sessions

  1. History of Artificial Intelligence 
  •  Turing Test 
  •  Perceptron
  •  First AI Winter 
  •  Backpropagation Algorithm
  •  Second AI Winter
  •   Post AI Winter 
  •   AI Spring 
  1. Applications of Deep Learning
  • Speech Recognition 
  •  Natural Language Processing 
  •  Automation 
  •  Medical Diagnostics 
  •  Facial Analysis 
  •  Content Creation
  1. Mathematics of Neural Networks 
  • Mathematical definition of Classifier, Training and Iteration 
  •  Forward Pass 
  • Loss Function 
  •  Backpropagation 
  •  Deep Learning as Optimization Problem 
  •  Gradient Descent and Weights Update 
  •  Stochastic GD and Mini-Batch GD 
  • ADAM
  • Overfitting, Bias and Variance & Regularization
  1. Convolutional Neural Network

Types of layers 

  •  Convolutional Layer
  •  Activation Layer 
  • Pooling Layer 
  • Fully Connected Layer
  1. Evaluation Metrics 
  •  Confusion Matrix 
  •  Accuracy 
  •  Precision 
  •  Recall 
  •  Specificity 
  •  F1 Score 
  •  ROC Curve, AUC ROC
  1. Image Classification Architecture 
  •  LetNet
  •  AlexNet 
  •  VGG-16 
  •  GoogleNet 
  •  Resnet 
  •  Comparison of methods

Module 2: Basic Image Operations and Getting Started

  1. Course Introduction and Setup
  • Introduction to Computer Vision and OpenCV 
  • Installation of OpenCV & Python on Windows 
  1. Basics of Computer Vision and OpenCV
  • What are Images? 
  • How are Images Formed? 
  • Storing Images on Computers 
  • Getting Started with OpenCV – A Brief OpenCV Intro 
  • Grayscaling – Converting Color Images To Shades of Gray 
  • Understanding Color Spaces – The Many Ways Color Images Are Stored Digitally 
  • Histogram representation of Images – Visualizing the Components of Images 
  • Creating Images & Drawing on Images – Make Squares, Circles, Polygons & Add Text 
  1. Image Manipulations & Processing
  • Transformations, Affine And Non-Affine – The Many Ways We Can Change Images 
  • Image Translations – Moving Images Up, Down. Left And Right 
  • Rotations – How To Spin Your Image Around And Do Horizontal Flipping 
  • Scaling, Re-sizing and Interpolations – Understand How Re-Sizing Affects Quality 
  • Image Pyramids – Another Way of Re-Sizing 
  • Cropping – Cut Out The Image The Regions You Want or Don’t Want 
  • Arithmetic Operations – Brightening and Darkening Images 
  • Bitwise Operations – How Image Masking Works 
  • Blurring – The Many Ways We Can Blur Images & Why It’s Important 
  • Sharpening – Reverse Your Images Blurs 
  • Thresholding (Binarization) – Making Certain Images Areas Black or White 
  • Dilation, Erosion, Opening/Closing – Importance of Thickening/Thinning Lines 
  • Edge Detection using Image Gradients & Canny Edge Detection 
  • Perspective & Affine Transforms – Take An Off Angle Shot & Make It Look Top Down 
  1. Image Segmentation & Contours
  • Segmentation and Contours – Extract Defined Shapes In Your Image 
  • Sorting Contours – Sort Those Shapes By Size 
  • Approximating Contours & Finding Their Convex Hull – Clean Up Messy Contours 
  • Matching Contour Shapes – Match Shapes In Images Even When Distorted 
  • Line Detection – Detect Straight Lines E.g. The Lines On A Sudoku Game 
  • Circle Detection 
  • Blob Detection – Detect The Center of Flowers
  1. Facial Landmark Detection 
  • Introduction to Dlib 
  • Facial Landmarks Detection using dlib 
  • Application – Face Alignment 
  • Improving Speed of Facial Landmark Detector 
  • Improving accuracy of Facial Landmark Detector 
  • Train a custom Facial Landmark Detector 

 Research Paper review. 

  1.  Applications of Facial Landmarks 
  • Alpha Blending & Seamless Cloning 
  • Affine and Perspective Transformations 
  • Delaunay Triangulation 
  • Face Averaging 
  • Face Morphing 
  • Face Swap 
  • Head Pose Estimation 
  • Blink Detection and Drowsy Driver Detection
  1. Face Recognition 
  • Face Recognition Overview 
  • Eigen Faces 
  • Fisher Faces 
  • Local Binary Patterns Histograms 
  • PCA and LDA 
  • Deep Metric Learning 
  • Deep Learning based Face Recognition

Module 3: Understanding Object Detection

  1. Traditional Approach to Object Detection 
  •  Background Subtraction 
  •  Sliding Window 
  •  Selective Approach 
  •  Traditional ML
  1. Single stage Object Detection 
  •  Main pipeline 
  •  YOLO 
  •  SSD 
  •  RetinaNet 
  1. Two Stage Object Detection
  •  R-CNN 
  •  Fast-RCNN 
  •  Faster-RCNN 
  •  Comparison between Fast-RCNN and Faster-RCNN 

Project: Image Captioning Project with the concepts learnt.

  1. Object Tracking & Motion Analysis
  • Filtering by Color 
  • Background Subtraction and Foreground Subtraction 
  • Using Meanshift for Object Tracking 
  • Using CAMshift for Object Tracking 
  • Optical Flow – Track Moving Objects In Videos 
  • Ball Tracking 
  1. Computational Photography & Make a License Plate Reader
  • Hough Transforms 
  • High Dynamic Range Imaging 
  • Seamless Cloning 
  • mage Inpainting 
  1. Build a Credit Card Number Reader
  • Creating a Credit Card Number Dataset 
  • Training Our Model
  • Extracting A Credit Card from the Background 
  • Use our Model to Identify the Digits & Display it onto our Credit Card

GANs

Introduction to GAN,

Working of GAN(In detail),

Application, Vanilla GAN,

Generate Handwritten Digits,

Progressive GAN,

Generating Realistic Faces,

Generate Videos from other Videos

INDUSTRY PROJECTS

We Provide 4-5 Projects during the course , and various industry level use case for practice.
Students get one to one mentoring while solving various industry level projects too
Real world industry Projects and deployment in AWS and Azure Cloud

Sample Certificate:

 

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