Learn Raspberry Pi, 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. 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