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MATLAB Programming 1

Complete MATLAB Programming – Beginners to Advance Level

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Complete MATLAB Programming – Beginners to Advance Level

200 days
All levels
104 lessons
0 quizzes
27 students

This Certified Course in MATLAB Programming provides the Learners with the Complete Assistance in Learning MATLAB Programming with ease.

Curriculum Designed suitably to enable Learners begin from Scratch !

Module I : Image Processing Fundamentals

Module II : Image Compression & Segmentation

Module III : Algorithmic Approach in MATLAB

Module IV : Project Based Learning

Module V : Machine Learning Algorithms in MATLAB

Module VI : Detection & Classification Applications

Module : VII : Regression & Data Analysis using MATLAB

Module VIII : Deep Learning Implementation using MATLAB

Module IX :  NLP & AI Implementation in MATLAB

Module X : Capstone Projects  

CATEGORY – I IMAGE PROCESSING USING MATLAB
  • Lesson 1 : Introduction to Image Processing & Applications
  • Lesson 2 : MATLAB Fundamentals & Tool Box
  • Lesson 3 :  GUI , Graphs & Plots in MATLAB 
  • Lesson 4 : Graphical User Interface – I
  • Lesson 5 :  Graphical User Interface – II
  • Lesson 6 : Commands , Control Statements & Loops in MATLAB
  • Lesson 7 : Basic Image Manipulation
  • Lesson 8 : Image Blurring , De-Blurring & Transformation
  • Lesson 9 : Image Erosion , Dilution & Fusion
  • Lesson 10 : De Noising in Images
  • Lesson 11 : Filtering in Images
  • Lesson 12 : Image Compression – DWT & SWT
  • Lesson 13 : Image Compression – SWT & Watermarking
  • Lesson 14 : Feature Extraction in Images
  • Lesson 15 : GLCM Based Feature Extraction
  • Lesson 16 : Image Segmentation – K Means
  • Lesson 17 : Image Segmentation – APP
  • Lesson 18 : Image Segmentation – OTSU Thresholding
  • Lesson 19 : Image Based Clustering
  • Lesson 20 : Image Segmentation – Watershed
  • Lesson 21 : Texture Based Segmentation using Gabor Filter
  • Lesson 22 : Edge Detection in Images
  • Lesson 23: Face Detection using HAAR Cascade
  • Lesson 24 : AES Based Image Encryption
  • Lesson 25 : RSA Algorithm Based Image Encryption
  • Lesson 26 : Image Pattern Recognition
  • Lesson 27 : Training Image Datasets
  • Lesson 28 : Neural Networks in Images
  • Lesson 29 : Project Demo – I
  • Lesson 30 : Project Demo – II

CATEGORY – II MACHINE LEARNING IN MATLAB  

  • Lesson 1 : Introduction to Machine Learning
  • Lesson 2: How to Import & Pre Process Data
  • Lesson 3: Prediction using Linear Regression
  • Lesson 4 : Logical Classification using Logistic Regression
  • Lesson 5 : Object Classification using SVM 
  • Lesson 6 : Predictions using Naive Bayes
  • Lesson 7 : Finding Nearest Neighbor using KNN
  • Lesson 8 : Data Clustering using K means
  • Lesson 9 : Random Forest Application
  • Lesson 10 : Dimensionality Reduction Algorithm
  • Lesson 11 : Regression using GBM
  • Lesson 12 : Data Analysis using XG Boost
  • Lesson 13 : Prediction using Light BGM
  • Lesson 14 : Prediction using CATBOOST
  • Lesson 15 :  Image Labelling Techniques
  • Lesson 16 : NN Based Image Classification
  • Lesson 17 : Image Classification  – KNN
  • Lesson 18 : Image Classification – SVM
  • Lesson 19 :  Project Demo – III
  • Lesson 20 :  Project Demo – IV

CATEGORY – III  ARTIFICIAL INTELLIGENCE  IN MATLAB  

  • Lesson 1 : Introduction to Deep Learning
  • Lesson 2 : Simple Labelling Techniques
  • Lesson 3 : Classification using CNN
  • Lesson 4 : Data Analysis using LSTM
  • Lesson 5 : Role of NLP in AI
  • Lesson 6 : Classification of Text Data using Deep Learning
  • Lesson 7 : Speech to Text Conversion
  • Lesson 8 : Audio Labelling in MATLAB
  • Lesson 9 : Project Demo – V
  • Lesson 10 : Project Demo – VI
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