Autonomous Cars: Deep Learning and Computer Vision in Python

Course Curriculum

Chapter 1: Environment Setup and Installation

Lecture 1: Introduction

Lecture 2: Important installation and troubleshooting notes

Lecture 3: Install Anaconda, OpenCV, Tensorflow, and the Course Materials

Lecture 4: Test your Environment with Real-Time Edge Detection in a Jupyter Notebook

Lecture 5: Udemy 101: Getting the Most From This Course

Lecture 6: Important note

Chapter 2: Introduction to Self-Driving Cars

Lecture 1: A Brief History of Autonomous Vehicles

Lecture 2: Course Overview and Learning Outcomes

Chapter 3: Python Crash Course [Optional]

Lecture 1: Python Basics: Whitespace, Imports, and Lists

Lecture 2: Python Basics: Tuples and Dictionaries

Lecture 3: Python Basics: Functions and Boolean Operations

Lecture 4: Python Basics: Looping and an Exercise

Lecture 5: Introduction to Pandas

Lecture 6: Introduction to MatPlotLib

Lecture 7: Introduction to Seaborn

Chapter 4: Computer Vision Basics: Part 1

Lecture 1: What is computer vision and why is it important?

Lecture 2: Humans vs. Computers Vision system

Lecture 3: what is an image and how is it digitally stored?

Lecture 4: [Activity] View colored image and convert RGB to Gray

Lecture 5: [Activity] Detect lane lines in gray scale image

Lecture 6: [Activity] Detect lane lines in colored image

Lecture 7: What are the challenges of color selection technique?

Lecture 8: Color Spaces

Lecture 9: [Activity] Convert RGB to HSV color spaces and merge/split channels

Lecture 10: Convolutions – Sharpening and Blurring

Lecture 11: [Activity] Convolutions – Sharpening and Blurring

Lecture 12: Edge Detection and Gradient Calculations (Sobel, Laplace and Canny)

Lecture 13: [Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny)

Lecture 14: [Activity] Project #1: Canny Sobel and Laplace Edge Detection using Webcam

Chapter 5: Computer Vision Basics: Part 2

Lecture 1: Image Transformation – Rotations, Translation and Resizing

Lecture 2: [Activity] Code to perform rotation, translation and resizing

Lecture 3: Image Transformations – Perspective transform

Lecture 4: [Activity] Perform non-affine image transformation on a traffic sign image

Lecture 5: Image cropping dilation and erosion

Lecture 6: [Activity] Code to perform Image cropping dilation and erosion

Lecture 7: Region of interest masking

Lecture 8: [Activity] Code to define the region of interest

Lecture 9: Hough transform theory

Lecture 10: [Activity] Hough transform – practical example in python

Lecture 11: Project Solution: Hough transform to detect lane lines in an image

Chapter 6: Computer Vision Basics: Part 3

Lecture 1: Image Features and their importance for object detection

Lecture 2: [Activity] Find a truck in an image manually!

Lecture 3: Template Matching – Find a Truck

Lecture 4: [Activity] Project Solution: Find a Truck Using Template Matching

Lecture 5: Corner detection – Harris

Lecture 6: [Activity] Code to perform corner detection

Lecture 7: Image Scaling – Pyramiding up/down

Lecture 8: [Activity] Code to perform Image pyramiding

Lecture 9: Histogram of colors

Lecture 10: [Activity] Code to obtain color histogram

Lecture 11: Histogram of Oriented Gradients (HOG)

Lecture 12: [Activity] Code to perform HOG Feature extraction

Lecture 13: Feature Extraction – SIFT, SURF, FAST and ORB

Lecture 14: [Activity] FAST/ORB Feature Extraction in OpenCV

Chapter 7: Machine Learning: Part 1

Lecture 1: What is Machine Learning?

Lecture 2: Evaluating Machine Learning Systems with Cross-Validation

Lecture 3: Linear Regression

Lecture 4: [Activity] Linear Regression in Action

Lecture 5: Logistic Regression

Lecture 6: [Activity] Logistic Regression In Action

Lecture 7: Decision Trees and Random Forests

Lecture 8: [Activity] Decision Trees In Action

Chapter 8: Machine Learning: Part 2

Lecture 1: Bayes Theorem and Naive Bayes

Lecture 2: [Activity] Naive Bayes in Action

Lecture 3: Support Vector Machines (SVM) and Support Vector Classifiers (SVC)

Lecture 4: [Activity] Support Vector Classifiers in Action

Lecture 5: Project Solution: Detecting Cars Using SVM – Part #1

Lecture 6: [Activity] Detecting Cars Using SVM – Part #2

Lecture 7: [Activity] Project Solution: Detecting Cars Using SVM – Part #3

Chapter 9: Artificial Neural Networks

Lecture 1: Introduction: What are Artificial Neural Networks and how do they learn?

Lecture 2: Single Neuron Perceptron Model

Lecture 3: Activation Functions

Lecture 4: ANN Training and dataset split

Lecture 5: Practical Example – Vehicle Speed Determination

Lecture 6: Code to build a perceptron for binary classification

Lecture 7: Backpropagation Training

Lecture 8: Code to Train a perceptron for binary classification

Lecture 9: Two and Multi-layer Perceptron ANN

Lecture 10: Example 1 – Build Multi-layer perceptron for binary classification

Lecture 11: Example 2 – Build Multi-layer perceptron for binary classification

Chapter 10: Deep Learning and Tensorflow: Part 1

Lecture 1: Intro to Deep Learning and Tensorflow

Lecture 2: Building Deep Neural Networks with Keras, Normalization, and One-Hot Encoding.

Lecture 3: [Activity] Building a Logistic Classifier with Deep Learning and Keras

Lecture 4: ReLU Activation, and Preventing Overfitting with Dropout Regularlization

Lecture 5: [Activity] Improving our Classifier with Dropout Regularization

Chapter 11: Deep Learning and Tensorflow: Part 2

Lecture 1: Convolutional Neural Networks (CNN's)

Lecture 2: Implementing CNN's in Keras

Lecture 3: [Activity] Classifying Images with a Simple CNN, Part 1

Lecture 4: [Activity] Classifying Images with a Simple CNN, Part 2