Learn Autonomous Cars Deep Learning and Computer Vision in Python Free Video Course

Learn Autonomous Cars OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars

Autonomous Cars

What you will 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

Requirements of Autonomous Cars:

  • 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

Description of Autonomous Cars:

Autonomous Cars Self-ruling Cars: Computer Vision and Deep Learning

Autonomous Cars The car business is encountering a change in outlook from traditional, human-driven vehicles into self-driving, computerized reasoning fueled vehicles. Self-driving vehicles offer a protected, proficient, and practical arrangement that will drastically reclassify the fate of human versatility. Self-driving vehicles are required to save over a large portion of 1,000,000 lives and create gigantic financial freedoms in abundance of $1 trillion dollars by 2035. The auto business is on a billion-dollar journey to send the most innovatively progressed vehicles out and about.

As the world advances towards a driverless future, the requirement for experienced designers and scientists in this arising new field has never been more essential.

The motivation behind this course is to furnish understudies with information on key parts of plan and advancement of self-driving vehicles. The course gives understudies viable involvement with different self-driving vehicles ideas, for example, AI and PC vision. Ideas like path recognition, traffic sign arrangement, vehicle/object discovery, man-made reasoning, and profound learning will be introduced. The course is focused towards understudies needing to acquire a crucial comprehension of self-driving vehicles control. Fundamental information on writing computer programs is suggested. Nonetheless, these points will be widely covered during early course addresses; thusly, the course has no requirements, and is available to any understudy with essential programming information. Understudies who select this self-driving vehicle course will dominate driverless vehicle advancements that will reshape the eventual fate of transportation.

Apparatuses and calculations we will cover include:

  • OpenCV
  • Profound Learning and Artificial Neural Networks
  • Convolutional Neural Networks
  • Format coordinating
  • Hoard include extraction
  • Filter, SURF, FAST, and ORB
  • Tensorflow and Keras
  • Direct relapse and calculated relapse
  • Choice Trees
  • Backing Vector Machines
  • Credulous Bayes

Your educators are Dr. Ryan Ahmed with a PhD in designing zeroing in on electric vehicle control frameworks, and Frank Kane, who went through 9 years at Amazon having some expertise in AI. Together, Frank and Dr. Ahmed have instructed more than 200,000 understudies all throughout the planet on Udemy alone.

Understudies of our mainstream course, “Information Science, Deep Learning, and Machine Learning with Python” may discover a portion of the subjects to be an audit of what was covered there, seen through the perspective of self-driving vehicles. However, a large portion of the course centers around points we’ve never covered, explicit to PC vision strategies utilized in self-sufficient vehicles. There are a lot of new, significant abilities to be mastered here!

Who this course is for:

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

Course content of Autonomous Cars:

Introduction of Autonomous Cars:

  • Install Anaconda, OpenCV, Tensorflow, and the Course Materials
  • Test your Environment with Real-Time Edge Detection in a Jupyter Notebook
  • Udemy 101: Getting the Most From This Course

Introduction to Self-Driving Cars:

  • A Brief History of Autonomous Vehicles
  • Course Overview and Learning Outcomes

Python Crash Course [Optional]:

  • Python Basics: Whitespace, Imports, and Lists
  • Python Basics: Tuples and Dictionaries
  • Python Basics: Functions and Boolean Operations
  • Python Basics: Looping and an Exercise
  • Introduction to Pandas
  • Introduction to MatPlotLib
  • Introduction to Seaborn

Computer Vision Basics: Part :

  • What is computer vision and why is it important?
  • Humans vs. Computers Vision system
  • what is an image and how is it digitally stored?
  • What are the challenges of color selection technique?
  • Color Spaces
  • Convolutions – Sharpening and Blurring
  • [Activity] Convolutions – Sharpening and Blurring
  • Edge Detection and Gradient Calculations (Sobel, Laplace and Canny)

Computer Vision Basics: Part 2:

  • Image Transformation – Rotations, Translation and Resizing
  • Code to perform rotation, translation and resizing
  • Image Transformations – Perspective transform
  • Perform non-affine image transformation on a traffic sign image
  • Image cropping dilation and erosion
  • Code to perform Image cropping dilation and erosion
  • Region of interest masking
  • Code to define the region of interest
  • Hough transform theory
  • Hough transform – practical example in python
  • Project Solution: Hough transform to detect lane lines in an image

Computer Vision Basics: Part 3:

  • Image Features and their importance for object detection
  • Find a truck in an image manually!
  • Template Matching – Find a Truck
  • Project Solution: Find a Truck Using Template Matching
  • Corner detection – Harris
  • Code to perform corner detection
  • Image Scaling – Pyramiding up/down
  • Code to perform Image pyramiding
  • Histogram of colors
  • Code to obtain color histogram
  • Histogram of Oriented Gradients (HOG)
  • Code to perform HOG Feature extraction
  • Feature Extraction – SIFT, SURF, FAST and ORB
  • FAST/ORB Feature Extraction in OpenCV

Machine Learning: Par:

  • What is Machine Learning?
  • Evaluating Machine Learning Systems with Cross-Validation
  • Linear Regression
  • Linear Regression in Action
  • Logistic Regression
  • Logistic Regression In Action
  • Decision Trees and Random Forests
  • [Activity] Decision Trees In Action

Machine Learning: Part:

  • Bayes Theorem and Naive Bayes
  • [Activity] Naive Bayes in Action
  • Support Vector Machines (SVM) and Support Vector Classifiers (SVC)
  • Support Vector Classifiers in Action
  • Project Solution: Detecting Cars Using SVM – Part #1
  • Detecting Cars Using SVM – Part #2
  • Project Solution: Detecting Cars Using SVM – Part #3

Artificial Neural Networks:

  • Introduction: What are Artificial Neural Networks and how do they learn?
  • Single Neuron Perceptron Model
  • Activation Functions
  • ANN Training and dataset split
  • Practical Example – Vehicle Speed Determination
  • Code to build a perceptron for binary classification
  • Backpropagation Training
  • Code to Train a perceptron for binary classification
  • Two and Multi-layer Perceptron ANN
  • Build Multi-layer perceptron for binary classification

Deep Learning and Tensorflow: Part:

  • Intro to Deep Learning and Tensorflow
  • Building Deep Neural Networks with Keras, Normalization, and One-Hot Encoding
  • Building a Logistic Classifier with Deep Learning and Keras
  • ReLU Activation, and Preventing Overfitting with Dropout Regularlization
  • Improving our Classifier with Dropout Regularization

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