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Image Processing Masterclass

Image Processing Masterclass

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257 students

MATLAB stands out from the rest when it comes to Image Processing 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 the Imaging Concepts 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: 25 Hrs            Modules: 7               Assignments: 15                            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 5:  Image Segmentation & Detection

Module 6: Cryptography and Data Hiding in Images

Module 7: Image Recognition & NN Applications

Module 8: Capstone Project


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

Key Learning Objectives:  This Module will introduce the participants 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 )


Module 2:  Digital 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 

Types of Images – Data Types – Images as Arrays – Import – Visualize and Extract Information from Images – Sampling & Quantisation of Images – Introductions to Pixels & Operations on Pixels

Lesson 4:  Image Processing Techniques & Conversions 

Image Enhancement – Image Blurring – Image De blurring – Transformation – RGB Conversion – Grey Scale Conversion of Images – Discrete Wavelet Transform – Discrete Fourier Transform – Algorithmic Applications on Images


Module 3: Image Pre Processing & Noise Removal in Images

Lesson 6: Data Pre Processing

Data Pre Processing – Data Loading for ML / DL Application – Reading data as csv – Numpy – Pandas – Understanding Data using Statistics – Key Terminologies – Class Distribution – L1 & L2 Normalisation – Data Visualisation Techniques

Lesson 7: Noise Removal in Images 

Deep Learning Algorithms – KERAS Fundamentals – Installation & Configuration – LSTM Algorithm – ANN Overview & Application – Demo

Lesson 8: Deep Learning Algorithms & KERAS

Deep Learning Algorithms – KERAS Fundamentals – Installation & Configuration – LSTM Algorithm – ANN Overview & Application.


Module 4: Neural Networks & Natural Language Processing

Key Learning Objectives:  Participants in this module will get the final insights into the advanced AI Prospects of Neural Networks and a much more advanced concept of NLP – Natural Language Processing that integrates the AI Concepts into Real time scenarios. This Blended Learning system provides contemplated prospects of AI with deep explanatory insights and illustrative Hands on Demo Sessions with assignments to provide participants a better insight into the domain.

 Lesson 9: Neural Networks – ANN & CNN

Neural Networks Introduction – ANN – CNN Concepts & Demo – Image Classification using CNN – Overview of GRU – Advanced Concepts of Neural Networks & Its Implications

Lesson 10: Natural Language Processing – Techniques & Process

Concepts of NLP –  Components & Process of Natural Language Processing – Installation of NLTK – Tokenisation – Stemming – Lemmatization –  Bag of Word ( BoW ) Model

LIVE PROJECT DEMO – Sentimental Analysis using Twitter Dataset



  • Prediction of Weather
  • Forecast of Ground Water Level
  • IPL Prediction
  • COVID Data Analysis & Prediction Model

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Image Processing Masterclass
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  • Price
  • Instructor Pantech eLearning
  • Duration 30 Hrs
  • Enrolled 257 student

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