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30-Day Money-Back Guarantee

This course includes:

  • 13 hours on-demand video
  • 2 articles
  • Full lifetime access
  • Access on mobile and TV
Development Software Engineering Computer Vision

Autonomous Cars: Deep Learning and Computer Vision in Python

Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars
Rating: 4.3 out of 54.3 (734 ratings)
6,885 students
Created by Sundog Education by Frank Kane, Frank Kane, Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard
Last updated 5/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Automatically detect lane markings in images
  • Detect cars and pedestrians using a trained classifier and with SVM
  • Classify traffic signs using Convolutional Neural Networks
  • Identify other vehicles in images using template matching
  • Build deep neural networks with Tensorflow and Keras
  • Analyze and visualize data with Numpy, Pandas, Matplotlib, and Seaborn
  • Process image data using OpenCV
  • Calibrate cameras in Python, correcting for distortion
  • Sharpen and blur images with convolution
  • Detect edges in images with Sobel, Laplace, and Canny
  • Transform images through translation, rotation, resizing, and perspective transform
  • Extract image features with HOG
  • Detect object corners with Harris
  • Classify data with machine learning techniques including regression, decision trees, Naive Bayes, and SVM
  • Classify data with artificial neural networks and deep learning
Curated for the Udemy for Business collection

Course content

12 sections • 93 lectures • 12h 45m total length

  • Preview03:26
  • Installation Notes: OpenCV3 and Python 3.7
    00:47
  • Preview05:31
  • Test your Environment with Real-Time Edge Detection in a Jupyter Notebook
    05:26
  • Udemy 101: Getting the Most From This Course
    02:10

  • A Brief History of Autonomous Vehicles
    Preview11:53
  • Course Overview and Learning Outcomes
    03:10

  • Python Basics: Whitespace, Imports, and Lists
    10:49
  • Python Basics: Tuples and Dictionaries
    06:08
  • Python Basics: Functions and Boolean Operations
    05:44
  • Python Basics: Looping and an Exercise
    05:03
  • Introduction to Pandas
    12:04
  • Introduction to MatPlotLib
    13:37
  • Introduction to Seaborn
    Preview17:55

  • What is computer vision and why is it important?
    Preview08:49
  • Humans vs. Computers Vision system
    10:36
  • what is an image and how is it digitally stored?
    08:44
  • [Activity] View colored image and convert RGB to Gray
    08:53
  • [Activity] Detect lane lines in gray scale image
    04:52
  • [Activity] Detect lane lines in colored image
    03:39
  • What are the challenges of color selection technique?
    03:45
  • Color Spaces
    10:07
  • [Activity] Convert RGB to HSV color spaces and merge/split channels
    17:36
  • Convolutions - Sharpening and Blurring
    07:33
  • [Activity] Convolutions - Sharpening and Blurring
    08:34
  • Edge Detection and Gradient Calculations (Sobel, Laplace and Canny)
    10:11
  • [Activity] Edge Detection and Gradient Calculations (Sobel, Laplace and Canny)
    07:23
  • [Activity] Project #1: Canny Sobel and Laplace Edge Detection using Webcam
    05:55

  • Preview06:01
  • [Activity] Code to perform rotation, translation and resizing
    12:11
  • Image Transformations – Perspective transform
    04:53
  • [Activity] Perform non-affine image transformation on a traffic sign image
    06:11
  • Image cropping dilation and erosion
    06:36
  • [Activity] Code to perform Image cropping dilation and erosion
    09:18
  • Region of interest masking
    04:46
  • [Activity] Code to define the region of interest
    07:23
  • Hough transform theory
    13:54
  • [Activity] Hough transform – practical example in python
    07:23
  • Project Solution: Hough transform to detect lane lines in an image
    11:29

  • Preview05:25
  • [Activity] Find a truck in an image manually!
    03:27
  • Template Matching - Find a Truck
    06:20
  • [Activity] Project Solution: Find a Truck Using Template Matching
    03:38
  • Corner detection – Harris
    06:36
  • [Activity] Code to perform corner detection
    09:52
  • Image Scaling – Pyramiding up/down
    03:07
  • [Activity] Code to perform Image pyramiding
    03:19
  • Histogram of colors
    02:05
  • [Activity] Code to obtain color histogram
    03:40
  • Histogram of Oriented Gradients (HOG)
    12:47
  • [Activity] Code to perform HOG Feature extraction
    04:27
  • Feature Extraction - SIFT, SURF, FAST and ORB
    03:01
  • [Activity] FAST/ORB Feature Extraction in OpenCV
    05:35

  • What is Machine Learning?
    Preview08:59
  • Evaluating Machine Learning Systems with Cross-Validation
    10:08
  • Linear Regression
    05:45
  • [Activity] Linear Regression in Action
    05:59
  • Logistic Regression
    03:03
  • Preview09:31
  • Decision Trees and Random Forests
    08:59
  • [Activity] Decision Trees In Action
    13:20

  • Bayes Theorem and Naive Bayes
    09:30
  • [Activity] Naive Bayes in Action
    08:59
  • Support Vector Machines (SVM) and Support Vector Classifiers (SVC)
    Preview06:14
  • [Activity] Support Vector Classifiers in Action
    08:08
  • Project Solution: Detecting Cars Using SVM - Part #1
    09:47
  • [Activity] Detecting Cars Using SVM - Part #2
    17:34
  • [Activity] Project Solution: Detecting Cars Using SVM - Part #3
    08:52

  • Introduction: What are Artificial Neural Networks and how do they learn?
    Preview12:20
  • Single Neuron Perceptron Model
    12:58
  • Activation Functions
    04:29
  • ANN Training and dataset split
    14:30
  • Practical Example - Vehicle Speed Determination
    06:26
  • Code to build a perceptron for binary classification
    10:02
  • Backpropagation Training
    07:16
  • Code to Train a perceptron for binary classification
    10:21
  • Two and Multi-layer Perceptron ANN
    07:14
  • Example 1 - Build Multi-layer perceptron for binary classification
    37:38
  • Example 2 - Build Multi-layer perceptron for binary classification
    09:22

  • Preview09:29
  • Building Deep Neural Networks with Keras, Normalization, and One-Hot Encoding.
    10:28
  • [Activity] Building a Logistic Classifier with Deep Learning and Keras
    13:46
  • ReLU Activation, and Preventing Overfitting with Dropout Regularlization
    05:57
  • [Activity] Improving our Classifier with Dropout Regularization
    04:21

Requirements

  • Windows, Mac, or Linux PC with at least 3GB free disk space.
  • Some prior experience in programming.

Description

Autonomous Cars: Computer Vision and Deep Learning

The automotive industry is experiencing a paradigm shift from conventional, human-driven vehicles into self-driving, artificial intelligence-powered vehicles. Self-driving vehicles offer a safe, efficient, and cost effective solution that will dramatically redefine the future of human mobility. Self-driving cars are expected to save over half a million lives and generate enormous economic opportunities in excess of $1 trillion dollars by 2035. The automotive industry is on a billion-dollar quest to deploy the most technologically advanced vehicles on the road.

As the world advances towards a driverless future, the need for experienced engineers and researchers in this emerging new field has never been more crucial.

The purpose of this course is to provide students with knowledge of key aspects of design and development of self-driving vehicles. The course provides students with practical experience in various self-driving vehicles concepts such as machine learning and computer vision. Concepts such as lane detection, traffic sign classification, vehicle/object detection, artificial intelligence, and deep learning will be presented. The course is targeted towards students wanting to gain a fundamental understanding of self-driving vehicles control. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this self-driving car course will master driverless car technologies that are going to reshape the future of transportation.

Tools and algorithms we'll cover include:

  • OpenCV

  • Deep Learning and Artificial Neural Networks

  • Convolutional Neural Networks

  • Template matching

  • HOG feature extraction

  • SIFT, SURF, FAST, and ORB

  • Tensorflow and Keras

  • Linear regression and logistic regression

  • Decision Trees

  • Support Vector Machines

  • Naive Bayes

Your instructors are Dr. Ryan Ahmed with a PhD in engineering focusing on electric vehicle control systems, and Frank Kane, who spent 9 years at Amazon specializing in machine learning. Together, Frank and Dr. Ahmed have taught over 200,000 students around the world on Udemy alone.

Students of our popular course, "Data Science, Deep Learning, and Machine Learning with Python" may find some of the topics to be a review of what was covered there, seen through the lens of self-driving cars. But, most of the course focuses on topics we've never covered before, specific to computer vision techniques used in autonomous vehicles. There are plenty of new, valuable skills to be learned here!

Who this course is for:

  • Software engineers interested in learning the algorithms that power self-driving cars.

Instructors

Sundog Education by Frank Kane
Founder, Sundog Education. Machine Learning Pro
Sundog Education by Frank Kane
  • 4.5 Instructor Rating
  • 95,731 Reviews
  • 431,486 Students
  • 22 Courses

Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. 

Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Due to our volume of students we are unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.

Frank Kane
Founder, Sundog Education
Frank Kane
  • 4.5 Instructor Rating
  • 92,452 Reviews
  • 387,485 Students
  • 14 Courses

Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.

Dr. Ryan Ahmed, Ph.D., MBA
Professor & Best-selling Udemy Instructor, 200K+ students
Dr. Ryan Ahmed, Ph.D., MBA
  • 4.5 Instructor Rating
  • 17,091 Reviews
  • 212,209 Students
  • 27 Courses

Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. 

Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 200,000+ students globally. He has over 15 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. 

Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.

* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.



Mitchell Bouchard
B.S, Host @RedCapeLearning 350,000 Students
Mitchell Bouchard
  • 4.5 Instructor Rating
  • 20,360 Reviews
  • 360,058 Students
  • 49 Courses

Mitch is a Canadian filmmaker from Harrow Ontario, Canada. In 2016 he graduated from Dakota State University with a B.S, in Computer Graphics specializing in Film and Cinematic Arts.

Currently, Mitch operates as the Chairman of Red Cape Studios, Inc. where he continues his passion for filmmaking. He is also the Host of Red Cape Learning and Produces / Directs content for Red Cape Films.

He has reached over 350,000 + Students on Udemy and Produced more than 3X Best-Selling Courses.

Mitch is currently working Producing Online Educational Courses thru Red Cape Studios Inc.

Winning several awards at Dakota State University such as "1st Place BeadleMania", "Winner College 10th Anniversary Dordt Film Festival" as well as "Outstanding Artist Award College of Arts and Sciences".

Mitch has been Featured on CBC's "Windsors Shorts" Tv Show and was also the Producer/Director for TEDX Windsor, featuring speakers from across the Country. 


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