Section 1 : Introduction
Lecture 1 Why This Course 00:01:30 Duration
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 2 : Installation
Lecture 1 Overview 00:00:25 Duration
Lecture 2 Anaconda Distribution - Mac 00:02:42 Duration
Lecture 3 Anaconda Distribution - Windows 00:02:54 Duration
Lecture 4 Text Editor 00:02:47 Duration
Lecture 5 Outro 00:00:29 Duration

Section 3 : Python Crash Course (Optional)
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 About Proctor Testing
Lecture 3 Python Crash Course Part 1 - Data Types 00:01:06 Duration
Lecture 4 Jupyter Notebooks 00:01:38 Duration
Lecture 5 Arithmetic Operations 00:04:23 Duration
Lecture 6 Variables 00:05:04 Duration
Lecture 7 Numeric Data Types 00:04:08 Duration
Lecture 8 String Data Types 00:05:45 Duration
Lecture 9 Booleans 00:04:27 Duration
Lecture 10 Methods 00:03:04 Duration
Lecture 11 Lists 00:05:29 Duration
Lecture 12 Slicing
Lecture 13 Membership Operators 00:02:49 Duration
Lecture 14 Mutability 00:04:08 Duration
Lecture 15 Mutability II 00:04:44 Duration
Lecture 16 Common Functions & Methods 00:07:30 Duration
Lecture 17 Tuples 00:03:31 Duration
Lecture 18 Sets 00:02:57 Duration
Lecture 19 Dictionaries 00:05:18 Duration
Lecture 20 Compound Data Structures 00:02:49 Duration
Lecture 21 Part 1 - Outro 00:00:14 Duration
Lecture 22 Part 2 - Control Flow 00:00:46 Duration
Lecture 23 If, else 00:04:46 Duration
Lecture 24 elif 00:06:51 Duration
Lecture 25 Complex Comparisons 00:05:10 Duration
Lecture 26 For Loops 00:07:16 Duration
Lecture 27 For Loops II 00:03:04 Duration
Lecture 28 While Loops 00:03:06 Duration
Lecture 29 Break 00:03:23 Duration
Lecture 30 Part 2 - Outro 00:00:16 Duration
Lecture 31 Part 3 - Functions 00:00:51 Duration
Lecture 32 Functions 00:05:34 Duration
Lecture 33 Scope
Lecture 34 Doc Strings 00:02:44 Duration
Lecture 35 Lambda & Higher Order Functions 00:06:06 Duration
Lecture 36 Part 3 - Outro 00:00:41 Duration

Section 4 : NumPy Crash Course (Optional)
Lecture 1 Overview 00:00:47 Duration
Lecture 2 Vector Addition - Arrays vs Lists 00:12:02 Duration
Lecture 3 Multidimensional Arrays 00:11:42 Duration
Lecture 4 One Dimensional Slicing 00:03:31 Duration
Lecture 5 Reshaping 00:03:34 Duration
Lecture 6 Multidimensional Slicing 00:07:19 Duration
Lecture 7 Manipulating Array Shapes 00:08:13 Duration
Lecture 8 Matrix Multiplication 00:04:18 Duration
Lecture 9 Stacking 00:13:57 Duration
Lecture 10 Part 4 - Outro 00:00:07 Duration

Section 5 : Computer Vision Finding Lane Lines
Lecture 1 Overview 00:00:35 Duration
Lecture 2 Image needed for the next lesson
Lecture 3 Loading Image 00:04:43 Duration
Lecture 4 About Proctor Testing
Lecture 5 Grayscale Conversion 00:04:29 Duration
Lecture 6 Smoothening Image 00:03:03 Duration
Lecture 7 Simple Edge Detection 00:04:18 Duration
Lecture 8 Region of Interest 00:07:39 Duration
Lecture 9 Binary Numbers & Bitwise_and 00:09:43 Duration
Lecture 10 Line Detection - Hough Transform 00:10:50 Duration
Lecture 11 Hough Transform II 00:13:19 Duration
Lecture 12 Optimizing
Lecture 13 Resource for upcoming video
Lecture 14 Finding Lanes on Video 00:06:16 Duration
Lecture 15 About Certification
Lecture 16 Source Code
Lecture 17 Part 5 - Conclusion 00:00:33 Duration

Section 6 : The Perceptron
Lecture 1 Overview 00:01:44 Duration
Lecture 2 Machine Learning 00:02:50 Duration
Lecture 3 Supervised Learning - Friendly Example 00:04:24 Duration
Lecture 4 Classification 00:07:47 Duration
Lecture 5 Linear Model
Lecture 6 Perceptrons 00:04:06 Duration
Lecture 7 Weights 00:02:02 Duration
Lecture 8 Project - Initial Stages 00:10:53 Duration
Lecture 9 Sample Code for Initial Stages
Lecture 10 Error Function 00:03:34 Duration
Lecture 11 Sigmoid 00:05:51 Duration
Lecture 12 Sigmoid Implementation (Code) 00:11:44 Duration
Lecture 13 Source code
Lecture 14 Cross Entropy 00:05:38 Duration
Lecture 15 Cross Entropy (Code) 00:07:40 Duration
Lecture 16 Source Code
Lecture 17 Gradient Descent 00:03:13 Duration
Lecture 18 Gradient Descent (Code) 00:08:44 Duration
Lecture 19 Recap 00:01:53 Duration
Lecture 20 Source Code
Lecture 21 Part 6 - Conclusion 00:00:39 Duration

Section 7 : Keras
Lecture 1 Overview 00:00:29 Duration
Lecture 2 Intro to Keras 00:02:04 Duration
Lecture 3 About Certification
Lecture 4 About Proctor Testing
Lecture 5 Starter Code
Lecture 6 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 7 Keras Models 00:21:08 Duration
Lecture 8 Keras - Predictions 00:19:20 Duration
Lecture 9 Source Code
Lecture 10 Part 7 - Outro 00:00:20 Duration

Section 8 : Deep Neural Networks
Lecture 1 Overview 00:00:51 Duration
Lecture 2 Non-Linear Boundaries 00:05:04 Duration
Lecture 3 Architecture 00:08:59 Duration
Lecture 4 Feedforward Process 00:07:44 Duration
Lecture 5 Error Function 00:04:09 Duration
Lecture 6 Backpropagation 00:05:09 Duration
Lecture 7 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 8 Code Implementation 00:25:59 Duration
Lecture 9 Source Code
Lecture 10 Section 8 - Conclusion 00:00:22 Duration

Section 9 : Multiclass Classification
Lecture 1 Overview 00:00:35 Duration
Lecture 2 Softmax 00:11:50 Duration
Lecture 3 Cross Entropy 00:08:14 Duration
Lecture 4 Implementation 00:30:54 Duration
Lecture 5 Source Code
Lecture 6 Section 9 - Outro 00:00:18 Duration

Section 10 : MNIST Image Recognition
Lecture 1 Overview 00:00:48 Duration
Lecture 2 MNIST Dataset 00:05:25 Duration
Lecture 3 Train & Test 00:13:27 Duration
Lecture 4 Hyperparameters 00:07:04 Duration
Lecture 5 Implementation Part 1 00:33:45 Duration
Lecture 6 About Certification
Lecture 7 Implementation Part 2 00:20:10 Duration
Lecture 8 Resource for upcoming video
Lecture 9 Implementation Part 3 00:11:48 Duration
Lecture 10 Final Source Code
Lecture 11 Section 10 - Outro 00:00:24 Duration

Section 11 : Convolutional Neural Networks
Lecture 1 Overview 00:00:45 Duration
Lecture 2 Convolutions & MNIST 00:06:44 Duration
Lecture 3 Convolutional Layer 00:18:11 Duration
Lecture 4 Convolutions II 00:08:06 Duration
Lecture 5 Pooling 00:14:10 Duration
Lecture 6 Fully Connected Layer 00:06:22 Duration
Lecture 7 Starter Code
Lecture 8 Code Implementation I 00:30:59 Duration
Lecture 9 Code Implementation II 00:26:19 Duration
Lecture 10 Final Source Code
Lecture 11 Section 11 - Conclusion 00:00:16 Duration

Section 12 : Classifying Road Symbols
Lecture 1 Overview 00:01:00 Duration
Lecture 2 Traffic Signs Starter Code
Lecture 3 Preprocessing Images 00:42:58 Duration
Lecture 4 leNet Implementation 00:20:11 Duration
Lecture 5 Fine-tuning Model 00:14:27 Duration
Lecture 6 Resources Needed for Testing
Lecture 7 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 8 Fit Generator 00:23:50 Duration
Lecture 9 Final Source Code
Lecture 10 Section 12 - Outro 00:00:42 Duration

Section 13 : Polynomial Regression
Lecture 1 Overview 00:00:29 Duration
Lecture 2 Implementation 00:15:22 Duration
Lecture 3 Final Source Code
Lecture 4 Section 13 - Conclusion 00:00:22 Duration

Section 14 : Behavioural Cloning
Lecture 1 Overview 00:03:11 Duration
Lecture 2 Collecting Data 00:17:45 Duration
Lecture 3 Downloading Data 00:17:52 Duration
Lecture 4 Balancing Data 00:11:31 Duration
Lecture 5 Training & Validation Split 00:11:26 Duration
Lecture 6 Preprocessing Images
Lecture 7 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 8 Defining Nvidia Model 00:27:09 Duration
Lecture 9 Drive
Lecture 10 About Certification
Lecture 11 Flask & Socket 00:17:33 Duration
Lecture 12 Self Driving Car - Test 1 00:16:30 Duration
Lecture 13 About Proctor Testing
Lecture 14 Generator - Augmentation Techniques 00:34:28 Duration
Lecture 15 Batch Generator 00:10:58 Duration
Lecture 16 Fit Generator 00:19:17 Duration
Lecture 17 Final Source Code
Lecture 18 Outro 00:00:54 Duration