Section 1 : Course Overview and Introduction

 		lecture 1 About Certification Pdf
lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
lecture 3 Course Curriculum Overview 4:40
lecture 4 Getting Set-Up for the Course Content 12:45

Section 2 : NumPy and Image Basics

 		lecture 5 Introduction to Numpy and Image Section 0:41
lecture 6 NumPy Arrays 16:49
lecture 7 What is an image 5:53
lecture 8 Images and NumPy 12:23
lecture 9 NumPy and Image Assessment Test 2:40
lecture 10 NumPy and Image Assessment Test - Solutions 8:46

Section 3 : Image Basics with Open CV

 		lecture 11 Introduction to Images and OpenCV Basics 2:37
lecture 12 Opening Image files in a notebook 19:30
lecture 13 Opening Image files with OpenCV 10:50
lecture 14 Drawing on Images - Part One - Basic Shapes 10:1
lecture 15 Drawing on Images Part Two - Text and Polygons 9:30
lecture 16 Direct Drawing on Images with a mouse - Part One 9:36
lecture 17 Direct Drawing on Images with a mouse - Part Two 2:42
lecture 18 Direct Drawing on Images with a mouse - Part Three 10:26
lecture 19 Image Basics Assessment 5:16
lecture 20 Image Basics Assessment Solutions

Section 4 : Image Processing

 		lecture 21 Introduction to Image Processing 0:40
lecture 22 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYS Pdf
lecture 23 Blending and Pasting Images 14:15
lecture 24 Blending and Pasting Images Part Two - Masks 15:56
lecture 25 Image Thresholding 17:41
lecture 26 Blurring and Smoothing 6:43
lecture 27 Blurring and Smoothing - Part Two 19:45
lecture 28 Morphological Operators 15:27
lecture 29 Gradients 13:40
lecture 30 Histograms - Part One 12:34
lecture 31 Histograms - Part Two - Histogram Eqaulization 12:20
lecture 32 Histograms Part Three - Histogram Equalization 8:13
lecture 33 Image Processing Assessment 3:52
lecture 34 Image Processing Assessment Solutions 8:31

Section 5 : Video Basics with Python and Open CV

 		lecture 35 Introduction to Video Basics 1:5
lecture 36 Connecting to Camera 14:14
lecture 37 Using Video Files 7:0
lecture 38 Drawing on Live Camera 16:46
lecture 39 Video Basics Assessment
lecture 40 Video Basics Assessment Solutions 5:1

Section 6 : Object Detection with Open CV and Python

 		lecture 41 Introduction to Object Detection 2:27
lecture 42 Template Matching 17:42
lecture 43 Corner Detection - Part One - Harris Corner Detect 14:9
lecture 44 Corner Detection - Part Two - Shi-Tomasi Detection 14:9
lecture 45 Edge Detection
lecture 46 Grid Detection 8:17
lecture 47 Contour Detection 11:11
lecture 48 Feature Matching - Part One 12:26
lecture 49 Feature Matching - Part Two 18:29
lecture 50 Watershed Algorithm - Part One.
lecture 51 Watershed Algorithm - Part Two 20:15
lecture 52 Custom Seeds with Watershed Algorithm 18:55
lecture 53 Introduction to Face Detection 9:12
lecture 54 Face Detection with OpenCV
lecture 55 Detection Assessment 3:27
lecture 56 Detection Assessment Solutions 7:11

Section 7 : Object Tracking

 		lecture 57 Introduction to Object Tracking 0:35
lecture 58 Optical Flow 5:38
lecture 59 Optical Flow Coding with OpenCV - Part One 18:35
lecture 60 Optical Flow Coding with OpenCV - Part Two 10:58
lecture 61 MeanShift and CamShift Tracking Theory 5:48
lecture 62 MeanShift and CamShift Tracking with OpenCV 14:42
lecture 63 Overview of various Tracking API Methods 6:50
lecture 64 Tracking APIs with OpenCV 6:53

Section 8 : Deep Learning for ComputerVision

 		lecture 65 Introduction to Deep Learning for Computer Vision 2:29
lecture 66 Machine Learning Basics 6:55
lecture 67 Understanding Classification Metrics 14:13
lecture 68 Introduction to Deep Learning Topics 1:25
lecture 69 Understanding a Neuron 5:13
lecture 70 Understanding a Neural Network 6:31
lecture 71 Cost Functions 3:40
lecture 72 Gradient Descent and Back Propagation 3:21
lecture 73 Keras Basics 18:2
lecture 74 MNIST Data Overview 4:41
lecture 75 Convolutional Neural Networks Overview - Part One 18:53
lecture 76 Convolutional Neural Networks Overview - Part Two 4:24
lecture 77 Keras Convolutional Neural Networks with MNIST 17:8
lecture 78 Keras Convolutional Neural Networks with CIFAR-10 11:59
lecture 79 LINK FOR CATS AND DOGS ZIP Zip
lecture 80 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
lecture 81 Deep Learning on Custom Images - Part Two 19:35
lecture 82 Deep Learning and Convolutional Neural Networks As 2:37
lecture 83 Deep Learning and Convolutional Neural Networks As 7:8
lecture 84 Introduction to YOLO v3 3:17
lecture 85 YOLO Weights Download
lecture 86 YOLO v3 with Python 17:5

Section 9 : Capstone Project

 		lecture 87 Introduction to CapStone Project 0:51
lecture 88 Capstone Part One - Variables and Background funct 7:47
lecture 89 Capstone Part Two - Segmentation 6:1
lecture 90 Capstone Part Three - Counting and ConvexHull 14:18
lecture 91 Capstone Part Four - Bringing it all together 12:15