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 :Â Image Processing Fundamentals
Module II :Â Image Compression & Segmentation
Module III :Â Algorithmic Approach in MATLAB
Module IV :Â Project Based Learning
Module V :Â Machine Learning Algorithms in MATLAB
Module VI :Â Detection & Classification Applications
Module : VII :Â Regression & Data Analysis using MATLAB
Module VIII :Â Deep Learning Implementation using MATLAB
Module IX :Â Â NLP & AI Implementation in MATLAB
Module X : Capstone Projects Â
CATEGORY – I IMAGE PROCESSING USING MATLAB
- Lesson 1 : Introduction to Image Processing & Applications
- Lesson 2 : MATLAB Fundamentals & Tool Box
- Lesson 3 :Â GUI , Graphs & Plots in MATLABÂ
- Lesson 4 : Graphical User Interface – I
- Lesson 5 :Â Â 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 MATLABÂ Â
- 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 SVMÂ
- 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 :Â Image Labelling Techniques
- Lesson 16 : NN Based Image Classification
- Lesson 17 : Image Classification – KNN
- Lesson 18 : Image Classification – SVM
- Lesson 19 :Â Â Project Demo – III
- Lesson 20 :Â Â Project Demo – IV
CATEGORY – IIIÂ ARTIFICIAL INTELLIGENCEÂ IN MATLABÂ Â
- 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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Orientation Video - Image Processing Matlab
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Lesson 1 - Introduction to Image processing & its Applications
-
Lesson 2 - MATLAB Fundamentals & Tools Box
-
Lesson 3 - GUI , Graphs & Plots in MATLAB
-
Lesson 4 - Graphical User Interface ( GUI - I ) in MATLAB
-
Lesson 5 - Graphical User Interface ( GUI - II ) in MATLAB
-
Lesson 6 - Commands , Control Statements & Loops in MATLAB
-
Lesson 7- Basic Image Manipulation
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Lesson 8-Morphological Image Pre-processing Techniques - Blurring , De Blurring & Transformation
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Lesson 9 - Morphological Image Pre-processing Techniques - Erosion , Dilution & Fusion
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Lesson 10 - De Noising Images
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Lesson 11 - Filters in Images
-
Lesson 12 - Image Compression - DWT & SWT Based
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Lesson 13 - Image Compression - SWT & Watermarking
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Lesson 14 - Feature Extraction in Images
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Lesson 15 - Feature Extraction using GLCM
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Lesson 16 - Image Segmentation - K Means
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Lesson 17 - Image Segmentation - APP
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Lesson 18 - Image segmentation - Otsu thresholding
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Lesson 19 - Clustering of Images - Region based Segmentation
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Lesson 20 - Image segmentation-watershed
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Lesson 21 - Texture segmentation using Gabor filter
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Lesson 22 - Edge Detection in Images
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Lesson 23 - Face detection using HAAR cascade
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Lesson 24 - Image Encryption - AES Algorithms
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Lesson 25 - RSA Algorithm based Image Encryption
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Lesson 26 - Image Pattern Recognition
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Lesson 27 - Training Datasets in Images
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Lesson 28 - Neural Networks in Images
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Project 1 - Image Hazy Noise Removal
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Project 2 - Currency Detection using MATLAB
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Lesson 31 : Introduction to Machine Learning
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Lesson 32: How to Import & Pre Process Data
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Lesson 33: Prediction using Linear Regression
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Lesson 34 : Logical Classification using Logistic Regression
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Lesson 35 : Object Classification using SVM
-
Lesson 36 : Predictions using Naive Bayes
-
Lesson 37 : Finding Nearest Neighbor using KNN
-
Lesson 38 : Data Clustering using K means
-
Lesson 39 : Random Forest Application
-
Lesson 40 : Dimensionality Reduction Algorithm
-
Lesson 41 : Regression using GBM
-
Lesson 42 : Data Analysis using XG Boost
-
Lesson 43 : Prediction using Light BGM
-
Lesson 44 : Prediction using CATBOOST
-
Lesson 45 : Image Labelling Techniques
-
Lesson 46 : NN Based Image Classification
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Lesson 47 : Image Classification - KNN
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Lesson 48 : Image Classification - SVM
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Lesson 51 : Introduction to Deep Learning in MATLAB
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Lesson 52 : Simple Labelling Techniques
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Lesson 53 : Classification using CNN
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Lesson 54 : Data Analysis using LSTM
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Lesson 55 : Role of NLP in AI
-
Lesson 56 : Classification of Text Data using Deep Learning
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Lesson 57 : Speech to Text Conversion
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Lesson 58 : Audio Labelling in MATLAB
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