Section 1 : Environment Setup and Installation
Lecture 1 Introduction copy 00:03:27 Duration
Lecture 2 Installation Notes OpenCV3 and Python 3
Lecture 3 Install Anaconda, OpenCV, Tensorflow, and the Course Materials 00:05:32 Duration
Lecture 4 Test your Environment with Real-Time Edge Detection in a Jupyter Notebook 00:05:26 Duration
Lecture 5 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 2 : Introduction to Self-Driving Cars
Lecture 1 A Brief History of Autonomous Vehicles 00:11:53 Duration
Lecture 2 Course Overview and Learning Outcomes 00:03:10 Duration

Section 3 : Python Crash Course [Optional]
Lecture 1 Python Basics Whitespace, Imports, and Lists 00:10:49 Duration
Lecture 2 Python Basics Tuples and Dictionaries 00:06:08 Duration
Lecture 3 Python Basics Functions and Boolean Operations 00:05:44 Duration
Lecture 4 Python Basics Looping and an Exercise 00:05:04 Duration
Lecture 5 Introduction to Pandas 00:12:04 Duration
Lecture 6 Introduction to MatPlotLib 00:13:38 Duration
Lecture 7 Introduction to Seaborn

Section 4 : Computer Vision Basics Part 1
Lecture 1 What is computer vision and why is it important 00:08:49 Duration
Lecture 2 Humans vs 00:10:37 Duration
Lecture 3 what is an image and how is it digitally stored 00:08:45 Duration
Lecture 4 [Activity] View colored image and convert RGB to Gray 00:08:53 Duration
Lecture 5 [Activity] Detect lane lines in gray scale image
Lecture 6 [Activity] Detect lane lines in colored image 00:03:39 Duration
Lecture 7 What are the challenges of color selection technique 00:03:45 Duration
Lecture 8 Color Spaces 00:10:08 Duration
Lecture 9 [Activity] Convert RGB to HSV color spaces and mergesplit channels
Lecture 10 Convolutions - Sharpening and Blurring 00:07:33 Duration
Lecture 11 [Activity] Convolutions - Sharpening and Blurring 00:08:34 Duration
Lecture 12 Edge Detection and Gradient Calculations (Sobel, Laplace and Canny) 00:10:11 Duration
Lecture 13 [Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny) 00:07:23 Duration
Lecture 14 [Activity] Project #1 Canny Sobel and Laplace Edge Detection using Webcam 00:05:56 Duration

Section 5 : Computer Vision Basics Part 2
Lecture 1 Image Transformation - Rotations, Translation and Resizing 00:06:01 Duration
Lecture 2 [Activity] Code to perform rotation, translation and resizing 00:12:11 Duration
Lecture 3 Image Transformations – Perspective transform 00:04:53 Duration
Lecture 4 [Activity] Perform non-affine image transformation on a traffic sign image 00:06:12 Duration
Lecture 5 Image cropping dilation and erosion 00:06:37 Duration
Lecture 6 [Activity] Code to perform Image cropping dilation and erosion 00:09:18 Duration
Lecture 7 Region of interest masking 00:04:47 Duration
Lecture 8 [Activity] Code to define the region of interest 00:07:23 Duration
Lecture 9 Hough transform theory 00:13:55 Duration
Lecture 10 [Activity] Hough transform – practical example in python 00:07:23 Duration
Lecture 11 Project Solution Hough transform to detect lane lines in an image 00:11:29 Duration

Section 6 : Computer Vision Basics Part 3
Lecture 1 Image Features and their importance for object detection 00:05:25 Duration
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 3 Template Matching - Find a Truck 00:06:20 Duration
Lecture 4 [Activity] Project Solution Find a Truck Using Template Matching 00:03:38 Duration
Lecture 5 Corner detection – Harris 00:06:36 Duration
Lecture 6 [Activity] Code to perform corner detection
Lecture 7 Image Scaling – Pyramiding updown 00:03:07 Duration
Lecture 8 [Activity] Code to perform Image pyramiding 00:03:19 Duration
Lecture 9 Histogram of colors 00:02:05 Duration
Lecture 10 [Activity] Code to obtain color histogram 00:03:40 Duration
Lecture 11 Histogram of Oriented Gradients (HOG) 00:12:47 Duration
Lecture 12 [Activity] Code to perform HOG Feature extraction 00:04:28 Duration
Lecture 13 Feature Extraction - SIFT, SURF, FAST and ORB 00:03:01 Duration
Lecture 14 [Activity] FASTORB Feature Extraction in OpenCV 00:05:35 Duration

Section 7 : Machine Learning Part 1
Lecture 1 What is Machine Learning 00:08:59 Duration
Lecture 2 Evaluating Machine Learning Systems with Cross-Validation 00:10:08 Duration
Lecture 3 Linear Regression 00:05:45 Duration
Lecture 4 [Activity] Linear Regression in Action 00:05:59 Duration
Lecture 5 Logistic Regression 00:03:03 Duration
Lecture 6 [Activity] Logistic Regression In Action 00:09:32 Duration
Lecture 7 Decision Trees and Random Forests 00:08:59 Duration
Lecture 8 [Activity] Decision Trees In Action 00:13:21 Duration

Section 8 : Machine Learning Part 2
Lecture 1 Bayes Theorem and Naive Bayes 00:09:30 Duration
Lecture 2 [Activity] Naive Bayes in Action 00:08:59 Duration
Lecture 3 Support Vector Machines (SVM) and Support Vector Classifiers (SVC) 00:06:15 Duration
Lecture 4 [Activity] Support Vector Classifiers in Action 00:08:09 Duration
Lecture 5 Project Solution Detecting Cars Using SVM - Part #1
Lecture 6 [Activity] Detecting Cars Using SVM - Part #2 00:17:34 Duration
Lecture 7 [Activity] Project Solution Detecting Cars Using SVM - Part #3 00:08:52 Duration

Section 9 : Artificial Neural Networks
Lecture 1 Introduction What are Artificial Neural Networks and how do they learn 00:12:20 Duration
Lecture 2 Single Neuron Perceptron Model 00:12:58 Duration
Lecture 3 Activation Functions 00:04:29 Duration
Lecture 4 ANN Training and dataset split 00:14:31 Duration
Lecture 5 Practical Example - Vehicle Speed Determination 00:06:27 Duration
Lecture 6 Code to build a perceptron for binary classification 00:10:02 Duration
Lecture 7 Backpropagation Training 00:07:16 Duration
Lecture 8 Code to Train a perceptron for binary classification 00:10:21 Duration
Lecture 9 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 10 Example 1 - Build Multi-layer perceptron for binary classification 00:37:39 Duration
Lecture 11 Example 2 - Build Multi-layer perceptron for binary classification 00:09:23 Duration

Section 10 : Deep Learning and Tensorflow Part 1
Lecture 1 Intro to Deep Learning and Tensorflow 00:09:29 Duration
Lecture 2 Building Deep Neural Networks with Keras, Norm 00:10:29 Duration
Lecture 3 [Activity] Building a Logistic Classifier with De 00:13:46 Duration
Lecture 4 ReLU Activation, and Preventing Overfitting with D 00:05:58 Duration
Lecture 5 [Activity] Improving our Classifier with Dropout R 00:04:21 Duration

Section 11 : Deep Learning and Tensorflow Part 2
Lecture 1 Convolutional Neural Networks (CNN's) 00:06:26 Duration
Lecture 2 Implementing CNN's in Keras 00:05:48 Duration
Lecture 3 [Activity] Classifying Images with a Simple CNN 00:08:06 Duration
Lecture 4 [Activity] Classifying Images with a Simple CNN, 00:07:45 Duration
Lecture 5 Max Pooling 00:02:36 Duration
Lecture 6 [Activity] Improving our CNN's Topology and with M 00:10:19 Duration
Lecture 7 [Activity] Build a CNN to Classify Traffic Signs 00:11:14 Duration
Lecture 8 [Activity] Build a CNN to Classify Traffic Siigns 00:18:00 Duration