Build Your Own Self Driving Car| [Course 1 & Course 2]

Learn Raspberry Pi 3, Arduino UNO, Image Processing and Neural Networks (Machine Learning) for any Embedded IOT Project

What you'll 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
Requirements
Basic Understanding of C or C++
Basic Understanding of Digital Logic
Basic Understanding of Soldering and Breadboard Prototyping
Description
"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. The complete Self driving Car project is divided into 2 PartsPart-1: (Course - 1)1. Learn to design complete hardware for self driving car   a. Learn to setup Master device ( Raspberry Pi 3 ) for any project   b. Learn to setup Slave device ( Arduino UNO ) for any project  c. Learn to Establish Communication link between Master and Slave device2. Learn Image Processing using OpenCV43. Learn to driver robot on road lanePart-2: (Course - 2)1. Learn Essentials of Machine Learning2. Learn to train your own cascade classifier to detect Stop Sign, Traffic Lights and any Object3. Learn to design LED Dynamic Turn Indicators"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. The complete Self driving Car project is divided into 2 Parts

Overview

Section 1: Introduction

Lecture 1 Course Curriculum

Lecture 2 Detailed Working

Section 2: Build Hardware for Self Driving Car

Lecture 3 Hardware Requirements (hardware Link is provided in Resource Section)

Lecture 4 Assemble Hardware Parts (Robot Chassis)

Lecture 5 How To Build Track for Testing

Section 3: Slave Device Setup (Arduino UNO)

Lecture 6 Forward & Backward Functions for Motors

Lecture 7 Left & Right Functions for Motors

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

Lecture 8 How to Flash Raspbian OS on Raspberry Pi 3 B+

Lecture 9 Raspbian Buster Fix

Lecture 10 Connect Raspberry PI to Personal Computer through Ethernet

Lecture 11 Connect Raspberry PI to Personal Computer through WiFi

Lecture 12 Connect Raspberry PI to Personal Computer through VNC Viewer

Section 5: Install OpenCV4 on Raspberry PI 3 B+

Lecture 13 Introduction to OpenCV

Lecture 14 Remove Unnecessary Software from Raspberry PI

Lecture 15 Clone OpenCV from GitHub

Lecture 16 Build OpenCV on Raspberry PI with CMake

Lecture 17 Setting Up Libraries in Programming Editor

Lecture 18 Test First Program In Geany Programming Editor

Lecture 19 SD CARD BACKUP

Section 6: Camera Setup for Raspberry PI

Lecture 20 Install Raspicam & Wiring PI Libraries on Raspberry PI

Lecture 21 Mount Camera on Robot Car Chassis

Lecture 22 Backup of SD Card

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

Lecture 23 How to Initialize Camera

Lecture 24 C++ Code to Capture Images

Lecture 25 C++ Code to Capture Video

Lecture 26 calculate FPS (Frames Per Second)

Section 8: Image Processing Using OpenCV4 & C++

Lecture 27 Convert Image Signature

Lecture 28 Create Region Of Interest

Lecture 29 Perspective Transformation (Bird Eye View)

Lecture 30 Threshold Operations

Lecture 31 Canny Edge Detection

Lecture 32 Troubleshoot Hardware & Software

Lecture 33 How to Find Lanes from Track

Lecture 34 Histogram and Vectors

Lecture 35 Iterators and Pointers

Lecture 36 Calibration

Lecture 37 Final Step

Section 9: Master & Slave Device Communication

Lecture 38 Raspberry PI Digital Pins

Lecture 39 Wiring Pi Library Fix (download latest command list in resource)

Lecture 40 Slave Device (Arduino Uno) Programming

Lecture 41 Testing

Lecture 42 Smooth Performance Tweek

Section 10: Final Testing & Features (Image Processing)

Lecture 43 Testing on Large Track

Lecture 44 Lane End & UTurn Implementation (Main Device)

Lecture 45 Lane End & UTurn Implementation (Slave Device)

Section 11: Introduction to Machine Learning

Lecture 46 Basic Steps & Terminologies

Section 12: (Stop Sign) Neural Network Training

Lecture 47 Creating Stop sign

Lecture 48 C++ code to Capture & Save Images

Lecture 49 Capturing Positive Samples for Stop sign

Lecture 50 Capturing Negative Samples

Lecture 51 Cascade Training Software and Image Cropping

Lecture 52 Training of Haar Cascade Model for Stop Sign

Section 13: (Stop Sign) Detection on Raspberry Pi3

Lecture 53 Load (.xml) file in C++ Code

Lecture 54 Writing Image Classifier Program in C++

Lecture 55 Stop Sign Detection Testing

Lecture 56 Create Linear Equations to Calculate Distance

Lecture 57 Solve Linear Equations & Distance Testing

Section 14: Stop Sign Detection Testing

Lecture 58 C++ Programming in Raspberry Pi

Lecture 59 C++ Programming in Arduino UNO

Lecture 60 Final Testing (Stop Sign)

Section 15: (Obstacle) Neural Network Training

Lecture 61 Positive Sample for Object

Lecture 62 Extracting Positive samples for Object

Lecture 63 Cascade Training for Object Detection

Section 16: Obstacle Detection on Raspberry Pi3

Lecture 64 C++ Code to Detect Object

Lecture 65 Create Linear Equations to Calculate Distance (for Object)

Lecture 66 Solve Linear Equations & Distance Testing (for object)

Section 17: Obstacle Detection Testing

Lecture 67 Arduino Programming

Lecture 68 Lane Change Operation at object Detection

Lecture 69 Final Testing (Object)

Section 18: Traffic Light Training

Lecture 70 Traffic Light Model

Lecture 71 Positive Sample for Red Light

Lecture 72 Negative Sample for Red Light

Lecture 73 Training Data

Lecture 74 Cascade Model for Red Light

Section 19: Traffic Light Detection

Lecture 75 Load (.xml) file in C++ Code

Lecture 76 Linear Equations With Calibration

Lecture 77 Finding Actual Distance

Section 20: Traffic Light Testing

Lecture 78 Arduino Programming & Final Testing

Section 21: LED Dynamic Turn Signal Indicator

Lecture 79 Schematic Diagram

Lecture 80 Clock Circuit Build

Lecture 81 Indicator Circuit Build

Lecture 82 C++ Code to Control the indicators

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