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
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 |