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Certification Program on MATLAB Programming – Imaging & ML Applications

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Certification Program on MATLAB Programming – Imaging & ML Applications

200 days
All levels
0 lessons
0 quizzes
28 students

Engineers and scientists worldwide use MATLAB for a wide range of applications, in Industries and Institutions, R & D Division and Productions including multiple concepts such as deep learning and machine learning, signal processing and communications, image and video processing, control systems, test and measurement and more.

Applications of MATLAB are widely applicable to almost all domains in the area of Research, Practice, and Implementation. This program brings out the complete Tools and Explores MATLAB sequentially segment-wise providing the participants a deeper insight into the applicational capabilities of MATLAB

This IETE, Ranchi, and DSIR associative Certification Program on MATLAB – Imaging & ML Applications draws more attention.

MATLAB stands out from the rest when it comes to AI in specific. That is the reason behind its vast usage and deployment by Industries and Research Organisations. This certified course on Image Processing using MATLAB focuses mainly on AI and Applications using MATLAB ToolBox.  At the end of the course, the participants will gain unlimited exposure and will effectively learn and deploy Image processing techniques for specific applications.


Total Duration: 30 Hrs            Modules: 9              Assignments: 14                         Capstone Project – 1  

Module 1: MATLAB Programming – Installation, Features & Tool Box

Module 2: Digital Image Fundamentals & Image Processing Techniques

Module 3:  Image Pre-processing & Noise Removal

Module 4:  Image Compression

Module 5:  Application of GLCM

Module 6:  Image Segmentation & Detection

Module 7: ML Algorithm

Module 8: Image Recognition & NN Applications


Module 1:  MATLAB Programming – Installation, Features & Tool Box

Key Learning Objectives:  This Module will introduce the participants to the Overview of MATLAB and its tool boxes pertaining to Imaging with LIVE Interactive Sessions and Hands on Programs.

Tools Covered: MATLAB

Lesson 1: MATLAB – Fundamentals & Tool Box

MATLAB Introduction –  Installation of MATLAB –  Matrices in MATLAB – Functions in MATLAB –  Save & Load Variables in MATLAB – Graphs & Plots in MATLAB – Customising Plots – Graphical User Interface in MATLAB – Creating Buttons in GUI.

Assignment 1:  Data Visualisation

Go the Extra Mile –  Design a Calculator using MATLAB – GUI

 Lesson 2:  MATLAB Programming 

MATLAB Commands – Control Statements – Loops in MATLAB (For, IF, While) – Loading Images – Basic Conversions – Introduction to Filters – Graphical User Interface in MATLAB (Level II) – Creating Other Specifications (GUI)

Assignment 2 –   Graph variation using slider

Other Related Materials   – Pdfs / PPT and Video Links        Software Download Links – MATLAB


Module 2:  Image Fundamentals & Image Processing Techniques

Key Learning Objectives:  Participants will get updated towards the basic and important Image Processing Techniques in this Module.

Tools Covered: MATLAB

 Lesson 3: Digital Image Processing & Basic Image Manipulation 

Types of Images – Import – Visualize and Extract Information from Images – Sampling & Quantisation of Images – RGB Conversion – Gray Scale Conversion of Images – Discrete Wavelet Transform – Discrete Fourier Transform

Assignment 3 –  Conversion of images using GUI


Module 3: Image Pre-Processing & De Noising using Filters

Key Learning Objectives:

Understand the concept of Pre-Processing of Images. Comes in handy when it is required to program MATLAB for specific Image-based applications and projects

Lesson 4: Morphological Image Pre-processing Techniques 

Image Enhancement – Image Blurring & De Blurring – Transformation – Image Erosion – Dilation & Fusion Techniques – Application Demo

Assignment 4 –    Combine two images into a single image

Go the Extra Mile – Image enhancement using the deblurring method

Lesson 5: De Noising & Filtering Images

Noise Estimation – Noise Reduction – Spatial Filters (Mean & Adaptive Filters) & Frequency Domain Filters (Band Pass Filters) – Noise reduction in Image (Demo & Hands-On)

Assignment 5 –  Apply median filter for salt & pepper noise

Go the Extra Mile –Remove noise from MRI Scan images


Module 4: Compression Techniques of Images in MATLAB

Key Learning Objectives:

Compressions play a significant role in MATLAB. Learn and apprehend the basic applications of Compression techniques in MATLAB and Apply the same on the Images

Lesson 6: Image Compression

Compression Concept – Image Coding & Decoding Model –  Lossless & Lossy Compression – DWT & DFT Based Compression – SWT Based Compression –Watermarking- Applications of Image Compression – MATLAB

Assignment 6 –  Compress an image using Discreet Wavelet transform

Go the Extra Mile –   Create watermark using Stationary Wavelet transform


Module 5: Application of GLCM in Image Processing

Lesson 7: GLCM – Application based Learning

Compressed Images – Fundamentals of Feature Extraction in Images

Assignment 7 –  Extract features of an image using DWT & GLCM


Module 6:  Image Segmentation & Detection

Key Learning Objectives:

Lesson 8: Segmentation & Image Detection Techniques

Image Segmentation – Concepts – Binary Based Image Segmentation – K Means Based Image Segmentation – Spatial Fuzzy Means clustering of Image Segmentation- Segmenting based on Colour – Region-based segmentation –  Hands-on Exercises –

Assignment 8 –  Segmentation of image using Binary

Go the Extra Mile –   Segmentation of image using K Means Algorithm


Module 7: ML Algorithms

Key Learning Objectives:

Lesson 9: Support Vector Machine

Intro to SVM-Prediction using CSV Dataset-Classification using an image dataset

Assignment 9- Multiple object detection using CSV dataset

Go the extra Mile – Brain tumor classification using an image dataset

Lesson 10: K-Nearest neighbour

Intro to KNN-Prediction using CSV Dataset-Classification using an image dataset

Assignment 10- Regression analysis using CSV dataset

Go the extra Mile – Brain tumor classification using an image dataset

Lesson 11: Linear regression

Intro to Linear Regression-Multiple Linear Regression-Prediction using CSV Dataset

Assignment 11- Covid-19 affected prediction using CSV dataset

 Lesson 12: Decision Tree

Intro to Decision Tree-Prediction using CSV Dataset

Assignment 12- Decision making a prediction using CSV dataset

Lesson 13: Naïve byes

Intro to Naïve byes-Prediction using CSV Dataset

Assignment 13- Attendance prediction using CSV dataset

Lesson 14: Logistic regression

Intro to Logistic regression -Prediction using CSV Dataset

Lesson 15: Random forest

Intro to Random Forest-Prediction using CSV Dataset

Lesson 16: XG Boost

Intro to XG Boost -Prediction using csv Dataset

Lesson 17: Light GBM

Intro to Light GBM -Prediction using csv Dataset


Module 8: Image Pattern Recognition & NN Applications

Key Learning Objectives:

Lesson 18: Image Recognition, Feature Extraction & Neural Networks

Fundamentals of Pattern Recognition – Data Processing – Training Datasets – Feature Extraction in Images – Image Labelling – Neural Networks in MATLAB – Applications of NN in Imaging Domain –

Assignment 14 –  Image recognition using deep neural networks

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  • Price
  • Instructor Pantech eLearning
  • Duration 30 Hrs
  • Lessons 0
  • Enrolled 28 student
  • Access 6 Months

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