Build Your own Self Driving Car - Deep Learning, OpenCV, C++

Build Your own Self Driving Car | Deep Learning, OpenCV, C++, available at $19.99, has an average rating of 4.15, with 47 lectures, based on 652 reviews, and has 4069 subscribers.

You will learn about Learn How to Setup Raspberry Pi 3 for any IOT Project Learn How to Setup Arduino UNO as a Slave micro-controller for any IOT Project Learn Image Processing using OpenCV4 for any Platform Learn Machine Learning & Train your own Image Classifier Learn How to Troubleshoot any Hardware & Software issues Most Important!! Learn to Design Embedded Product totally from scratch This course is ideal for individuals who are College or University student from Electronics/Electrical or Computer Engineering or relevant Diploma or Hobbyist interested in Machine Learning & Image Processing or Anybody Who wants to create Embedded IOT Project It is particularly useful for College or University student from Electronics/Electrical or Computer Engineering or relevant Diploma or Hobbyist interested in Machine Learning & Image Processing or Anybody Who wants to create Embedded IOT Project.

Enroll now: Build Your own Self Driving Car | Deep Learning, OpenCV, C++

Summary

Title: Build Your own Self Driving Car | Deep Learning, OpenCV, C++

Price: $19.99

Average Rating: 4.15

Number of Lectures: 47

Number of Published Lectures: 47

Number of Curriculum Items: 47

Number of Published Curriculum Objects: 47

Original Price: CA$24.99

Quality Status: approved

Status: Live

What You Will Learn

Learn How to Setup Raspberry Pi 3 for any IOT Project

Learn How to Setup Arduino UNO as a Slave micro-controller for any IOT Project

Learn Image Processing using OpenCV4 for any Platform

Learn Machine Learning & Train your own Image Classifier

Learn How to Troubleshoot any Hardware & Software issues

Most Important!! Learn to Design Embedded Product totally from scratch

Who Should Attend

College or University student from Electronics/Electrical or Computer Engineering or relevant Diploma

Hobbyist interested in Machine Learning & Image Processing

Anybody Who wants to create Embedded IOT Project

Target Audiences

College or University student from Electronics/Electrical or Computer Engineering or relevant Diploma

Hobbyist interested in Machine Learning & Image Processing

Anybody Who wants to create Embedded IOT Project

“Machine Learning will change the lives of all of us. What is Machine Learning? It’s behind what makes self-driving cars a reality”

This unique course is a complete walk-through process to Design, Build and Program a Embedded IOT Project (Self driving Car). Everything is discussed with details and clear explanation. Whole Project is divided into 2 parts.

(Course – 1)

1. Learn to design complete hardware for self driving car

   a. Learn to setup Master device ( Raspberry Pi ) for any project

   b. Learn to setup Slave device ( Arduino UNO ) for any project

  c. Learn to Establish Communication link between Master and Slave device

2. Learn Image Processing using OpenCV4

3. Learn to driver robot on road lanes

(Course – 2)

1. Learn Essentials of Machine Learning

2. Learn to train your own cascade classifier to detect Stop Sign, Traffic Lights and any Object

3. Learn to design LED Dynamic Turn Indicators

4. Create your GitHub Repository

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today’s leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Curriculum

Lecture 2: Machine Learning (Must Watch)

Lecture 3: Detailed Working

Chapter 2: Build Hardware for Self Driving Car

Lecture 1: Hardware Requirements (Hardware Link is provided in Resource Section)

Lecture 2: Assemble Hardware Parts (Robot Chassis) [ Circuit Diagram in resource section]

Lecture 3: How To Build Track for Testing

Chapter 3: Slave Device Setup (Arduino UNO)

Lecture 1: Forward & Backward Functions for Motors

Lecture 2: Left & Right Functions for Motors

Chapter 4: Master Device Setup (Raspberry PI 3 B+)

Lecture 1: How to Flash Raspbian OS on Raspberry Pi 3 B+

Lecture 2: Connect Raspberry PI to Personal Computer through Ethernet

Lecture 3: Connect Raspberry PI to Personal Computer through WiFi

Lecture 4: Connect Raspberry PI to Personal Computer through VNC Viewer

Lecture 5: Use My SD Card Backup

Chapter 5: Install OpenCV4 on Raspberry PI 3 B+

Lecture 1: Use My SD Card Backup

Lecture 2: Introduction to OpenCV

Lecture 3: Remove Unnecessary Software from Raspberry PI

Lecture 4: Clone OpenCV from GitHub

Lecture 5: Build OpenCV on Raspberry PI with CMake

Lecture 6: Setting Up Libraries in Programming Editor

Lecture 7: Test First Program In Geany Programming Editor

Chapter 6: Camera Setup for Raspberry PI

Lecture 1: Install Raspicam & Wiring PI Libraries on Raspberry PI

Lecture 2: Mount Camera on Robot Car Chassis

Lecture 3: Backup of SD Card

Chapter 7: C++ Code to Capture Images & Videos

Lecture 1: How to Initialize Camera

Lecture 2: C++ Code to Capture Images

Lecture 3: C++ Code to Capture Video

Lecture 4: calculate FPS (Frames Per Second)

Chapter 8: Image Processing Using OpenCV4 & C++

Lecture 1: Convert Image Signature

Lecture 2: Create Region Of Interest

Lecture 3: Perspective Transformation (Bird Eye View)

Lecture 4: Threshold Operations

Lecture 5: Canny Edge Detection

Lecture 6: Troubleshoot Hardware & Software

Lecture 7: How to Find Lanes from Track

Lecture 8: Histogram and Vectors

Lecture 9: Iterators and Pointers

Lecture 10: Calibration

Lecture 11: Final Step

Chapter 9: Master & Slave Device Communication

Lecture 1: Raspberry PI Digital Pins

Lecture 2: Wiring Pi Library Fix (download latest command list in resource)

Lecture 3: Slave Device (Arduino Uno) Programming

Lecture 4: Testing

Lecture 5: Smooth Performance Tweek

Chapter 10: Final Testing & Features

Lecture 1: Testing on Large Track

Lecture 2: Lane End & UTurn Implementation (Main Device)

Lecture 3: Lane End & UTurn Implementation (Slave Device)

Chapter 11: Last Step (Machine Learning)

Lecture 1: BONUS (Course 2)

Instructors

Rajandeep Singh
Embedded System Engineer

Rating Distribution

1 stars: 30 votes

2 stars: 18 votes

3 stars: 60 votes

4 stars: 229 votes

5 stars: 315 votes

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