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Internship on IoT With Machine Learning – IEEE Certification

12 weeks
All levels
0 lessons
0 quizzes
399 students

About the Program

This program has been initiated to enhance the knowledge from beginner to advance for the people who are interested in learning of advanced technologies like machine learning, artificial intelligence, deep learning etc…Other than learning, this program also provides an industrial exposure of working in these software’s and packages with its relevant application.

Technologies and Tools Covered


  • Python
  • Raspberry PI
  • IoT
  • Open CV
  • Keras
  • Tensorflow



  1. Raspberry Pi Architecture & Programming

Introduction to the Concept of IoT, Raspberry pi Controller, Python Programming Basics will be dealt with. Simple programs shall be done by the participants. This shall serve as the first step into the entry to the advanced concepts implementation using Raspberry pi




1 Introduction to IoT and the overview of its Applications
2 Overview of Raspberry pi Architecture and GPIO Pin Model
3 Introduction to Python Language
4 Python Basics (Hands On)
5  Interfacing led to the Raspberry pi(Hands On)
6 Interfacing Buzzers  to the Raspberry pi (Hands On)


II.            Sensor Interface and Open Source Cloud

Interfacing Sensors like Ultrasonic Sensor, IR Sensors, Fire Sensor to the Raspberry pi Computer, Getting Data from the Sensor, and Interfacing Open Source Cloud with Raspberry pi, and Uploading of Sensor Data to the cloud forms the crux of this day of Learning.




1 Introduction to Sensors & Raspberry Pi Interfacing
2 Working Principle of IR Sensor and Interface with Raspberry pi ( Hands On )
3 Working Principle of Ultrasonic Sensor and the interface with Raspberry pi ( Hands On )
4 Working Principle of Fire Sensor and the interface with Raspberry pi ( Hands On )
5 Upload Sensor data to the Think Speak Cloud ( Hands On )
6 Designing web Pages: Basics of HTML
7 Controlling Devices or appliances using webpage, Reading Input Status from sensor and monitoring on webpage ( Hands On )


3. Python & OpenCV programming

Introduction to the Concept of python and OpenCV will be dealt with. Simple programs shall be done by the participants. This shall serve as the first step into the entry to the advanced concepts implementation using python




1 Introduction to Image Processing
2 Python — Introduction

·       Python Strings

·       String Subscript and String slicing

·       If, For, While statements

·       While and else clause on loop

·       Break, continue and pass statement

3  Python on imaging

·       OpenCV library intro

·       Importing , Exporting and Visualizing images

·       Image Transformations

·       Type Conversions

·       Contrast Adjustment and Zooming & Pixel Info Analysis

Placing Lines Circles Texts In a Image

4 Image conversions

·       Image Plane Separation

·       Image Gray and B&W

·       HSV Conversions

Image block separation and fixing

5 Image filtering

·       Types of Noises in a Image

·       Blur in images

·       Median Filtering on noise removal

6 Region detection

·       Image Contour detection

·       Accessing Video and Storing

·       Accessing camera and storing

·       HSV Conversion based color detection

·       Remote camera accessing 


4. Machine Learning

Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.





1 Machine learning on image processing – an intro

·       Training Dataset.

·       Testing dataset.

·       Algorithms in ML

2. Face recognition in real time

·       Face detection

·       Dataset Creation

·       Testing data

·       DRLBP pattern recognition

3. Supervised Learning Methods-Linear Regression, Logistic Regression
4. K-Nearest Neighbors Algorithm on image
5. Working on Tensor Flows and Keras


Practical Assignments

Assignment 1: Image Transformations

Assignment 2: HSV Conversion based color detection

Assignment 3: Face detection


Training Methodology

The Program is mix of Theory sessions, Quizzes, Hands on Sessions, Liver Interaction with Experts, Assignments and Practical Exercises. Maximum Impetus is given to Hands on Sessions so as to enable the participants with the maximum knowledge transfer and satisfaction. The ratio of the theory, practical sessions will be 30:70.

Curriculum is empty
3 total





    Mugunthan S

    Very good experience both in theortical and pratical way.the conduction of task helps us to increase our knowledge about the coding.


    The method of teaching is very good I leaned a lot of things. Thank you pantech

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Internship on IoT With Machine Learning – IEEE Certification
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