Section 1 : Environment Setup and Installation

Lecture 1 Introduction copy 3:27
Lecture 2 Installation Notes OpenCV3 and Python 3 Text
Lecture 3 Install Anaconda, OpenCV, Tensorflow, and the Course Materials 5:32
Lecture 4 Test your Environment with Real-Time Edge Detection in a Jupyter Notebook 5:26
Lecture 5 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 2 : Introduction to Self-Driving Cars

Lecture 6 A Brief History of Autonomous Vehicles 11:53
Lecture 7 Course Overview and Learning Outcomes 3:10

Section 3 : Python Crash Course [Optional]

Lecture 8 Python Basics Whitespace, Imports, and Lists 10:49
Lecture 9 Python Basics Tuples and Dictionaries 6:8
Lecture 10 Python Basics Functions and Boolean Operations 5:44
Lecture 11 Python Basics Looping and an Exercise 5:4
Lecture 12 Introduction to Pandas 12:4
Lecture 13 Introduction to MatPlotLib 13:38
Lecture 14 Introduction to Seaborn

Section 4 : Computer Vision Basics Part 1

Lecture 15 What is computer vision and why is it important 8:49
Lecture 16 Humans vs 10:37
Lecture 17 what is an image and how is it digitally stored 8:45
Lecture 18 [Activity] View colored image and convert RGB to Gray 8:53
Lecture 19 [Activity] Detect lane lines in gray scale image
Lecture 20 [Activity] Detect lane lines in colored image 3:39
Lecture 21 What are the challenges of color selection technique 3:45
Lecture 22 Color Spaces 10:8
Lecture 23 [Activity] Convert RGB to HSV color spaces and mergesplit channels
Lecture 24 Convolutions - Sharpening and Blurring 7:33
Lecture 25 [Activity] Convolutions - Sharpening and Blurring 8:34
Lecture 26 Edge Detection and Gradient Calculations (Sobel, Laplace and Canny) 10:11
Lecture 27 [Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny) 7:23
Lecture 28 [Activity] Project #1 Canny Sobel and Laplace Edge Detection using Webcam 5:56

Section 5 : Computer Vision Basics Part 2

Lecture 29 Image Transformation - Rotations, Translation and Resizing 6:1
Lecture 30 [Activity] Code to perform rotation, translation and resizing 12:11
Lecture 31 Image Transformations – Perspective transform 4:53
Lecture 32 [Activity] Perform non-affine image transformation on a traffic sign image 6:12
Lecture 33 Image cropping dilation and erosion 6:37
Lecture 34 [Activity] Code to perform Image cropping dilation and erosion 9:18
Lecture 35 Region of interest masking 4:47
Lecture 36 [Activity] Code to define the region of interest 7:23
Lecture 37 Hough transform theory 13:55
Lecture 38 [Activity] Hough transform – practical example in python 7:23
Lecture 39 Project Solution Hough transform to detect lane lines in an image 11:29

Section 6 : Computer Vision Basics Part 3

Lecture 40 Image Features and their importance for object detection 5:25
Lecture 41 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 42 Template Matching - Find a Truck 6:20
Lecture 43 [Activity] Project Solution Find a Truck Using Template Matching 3:38
Lecture 44 Corner detection – Harris 6:36
Lecture 45 [Activity] Code to perform corner detection
Lecture 46 Image Scaling – Pyramiding updown 3:7
Lecture 47 [Activity] Code to perform Image pyramiding 3:19
Lecture 48 Histogram of colors 2:5
Lecture 49 [Activity] Code to obtain color histogram 3:40
Lecture 50 Histogram of Oriented Gradients (HOG) 12:47
Lecture 51 [Activity] Code to perform HOG Feature extraction 4:28
Lecture 52 Feature Extraction - SIFT, SURF, FAST and ORB 3:1
Lecture 53 [Activity] FASTORB Feature Extraction in OpenCV 5:35

Section 7 : Machine Learning Part 1

Lecture 54 What is Machine Learning 8:59
Lecture 55 Evaluating Machine Learning Systems with Cross-Validation 10:8
Lecture 56 Linear Regression 5:45
Lecture 57 [Activity] Linear Regression in Action 5:59
Lecture 58 Logistic Regression 3:3
Lecture 59 [Activity] Logistic Regression In Action 9:32
Lecture 60 Decision Trees and Random Forests 8:59
Lecture 61 [Activity] Decision Trees In Action 13:21

Section 8 : Machine Learning Part 2

Lecture 62 Bayes Theorem and Naive Bayes 9:30
Lecture 63 [Activity] Naive Bayes in Action 8:59
Lecture 64 Support Vector Machines (SVM) and Support Vector Classifiers (SVC) 6:15
Lecture 65 [Activity] Support Vector Classifiers in Action 8:9
Lecture 66 Project Solution Detecting Cars Using SVM - Part #1
Lecture 67 [Activity] Detecting Cars Using SVM - Part #2 17:34
Lecture 68 [Activity] Project Solution Detecting Cars Using SVM - Part #3 8:52

Section 9 : Artificial Neural Networks

Lecture 69 Introduction What are Artificial Neural Networks and how do they learn 12:20
Lecture 70 Single Neuron Perceptron Model 12:58
Lecture 71 Activation Functions 4:29
Lecture 72 ANN Training and dataset split 14:31
Lecture 73 Practical Example - Vehicle Speed Determination 6:27
Lecture 74 Code to build a perceptron for binary classification 10:2
Lecture 75 Backpropagation Training 7:16
Lecture 76 Code to Train a perceptron for binary classification 10:21
Lecture 77 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 78 Example 1 - Build Multi-layer perceptron for binary classification 37:39
Lecture 79 Example 2 - Build Multi-layer perceptron for binary classification 9:23

Section 10 : Deep Learning and Tensorflow Part 1

Lecture 80 Intro to Deep Learning and Tensorflow 9:29
Lecture 81 Building Deep Neural Networks with Keras, Norm 10:29
Lecture 82 [Activity] Building a Logistic Classifier with De 13:46
Lecture 83 ReLU Activation, and Preventing Overfitting with D 5:58
Lecture 84 [Activity] Improving our Classifier with Dropout R 4:21

Section 11 : Deep Learning and Tensorflow Part 2

Lecture 85 Convolutional Neural Networks (CNN's) 6:26
Lecture 86 Implementing CNN's in Keras 5:48
Lecture 87 [Activity] Classifying Images with a Simple CNN 8:6
Lecture 88 [Activity] Classifying Images with a Simple CNN, 7:45
Lecture 89 Max Pooling 2:36
Lecture 90 [Activity] Improving our CNN's Topology and with M 10:19
Lecture 91 [Activity] Build a CNN to Classify Traffic Signs 11:14
Lecture 92 [Activity] Build a CNN to Classify Traffic Siigns 18:0