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. 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 device2. Learn Image Processing using OpenCV43. Learn to driver robot on road lanes (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 Indicators4. Create your GitHub RepositoryMachine 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
Overview
Section 1: Introduction
Lecture 1 Course Curriculum
Lecture 2 Machine Learning (Must Watch)
Lecture 3 Detailed Working
Section 2: Build Hardware for Self Driving Car
Lecture 4 Hardware Requirements (Hardware Link is provided in Resource Section)
Lecture 5 Assemble Hardware Parts (Robot Chassis)[ Circuit Diagram in resource section]
Lecture 6 How To Build Track for Testing
Section 3: Slave Device Setup (Arduino UNO)
Lecture 7 Forward & Backward Functions for Motors
Lecture 8 Left & Right Functions for Motors
Section 4: Master Device Setup (Raspberry PI 3 B+)
Lecture 9 How to Flash Raspbian OS on Raspberry Pi 3 B+
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
Lecture 13 Use My SD Card Backup
Section 5: Install OpenCV4 on Raspberry PI 3 B+
Lecture 14 Use My SD Card Backup
Lecture 15 Introduction to OpenCV
Lecture 16 Remove Unnecessary Software from Raspberry PI
Lecture 17 Clone OpenCV from GitHub
Lecture 18 Build OpenCV on Raspberry PI with CMake
Lecture 19 Setting Up Libraries in Programming Editor
Lecture 20 Test First Program In Geany Programming Editor
Section 6: Camera Setup for Raspberry PI
Lecture 21 Install Raspicam & Wiring PI Libraries on Raspberry PI
Lecture 22 Mount Camera on Robot Car Chassis
Lecture 23 Backup of SD Card
Section 7: C++ Code to Capture Images & Videos
Lecture 24 How to Initialize Camera
Lecture 25 C++ Code to Capture Images
Lecture 26 C++ Code to Capture Video
Lecture 27 calculate FPS (Frames Per Second)
Section 8: Image Processing Using OpenCV4 & C++
Lecture 28 Convert Image Signature
Lecture 29 Create Region Of Interest
Lecture 30 Perspective Transformation (Bird Eye View)
Lecture 31 Threshold Operations
Lecture 32 Canny Edge Detection
Lecture 33 Troubleshoot Hardware & Software
Lecture 34 How to Find Lanes from Track
Lecture 35 Histogram and Vectors
Lecture 36 Iterators and Pointers
Lecture 37 Calibration
Lecture 38 Final Step
Section 9: Master & Slave Device Communication
Lecture 39 Raspberry PI Digital Pins
Lecture 40 Wiring Pi Library Fix (download latest command list in resource)
Lecture 41 Slave Device (Arduino Uno) Programming
Lecture 42 Testing
Lecture 43 Smooth Performance Tweek
Section 10: Final Testing & Features
Lecture 44 Testing on Large Track
Lecture 45 Lane End & UTurn Implementation (Main Device)
Lecture 46 Lane End & UTurn Implementation (Slave Device)
Section 11: Last Step (Machine Learning)
Lecture 47 BONUS (Course 2)
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



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