Why this course?

Description

Machine Learning using MATLAB involves leveraging MATLAB's Machine Learning Toolbox to design, train, and deploy machine learning models. Users begin by installing MATLAB, creating scripts, and loading their datasets. The toolbox supports various algorithms for tasks like classification, regression, and clustering. Essential steps include data preprocessing, feature engineering, splitting data for training/testing, model selection, training, prediction, and evaluation using metrics like accuracy and confusion matrices. MATLAB's visualization tools aid in result analysis, and users can fine-tune models for optimal performance. The deployment phase allows the integration of models into applications. MATLAB's interactive apps, like Classification Learner, simplify the process, making it an efficient platform for machine learning tasks.

MATLAB environment Machine Learning using MATLAB Programming

features: Machine Learning using MATLAB provides a comprehensive set of features and tools within the MATLAB environment, making it a powerful platform for developing, implementing, and deploying machine learning models.

Here are some key features: Machine Learning

Toolbox: MATLAB's Machine Learning Toolbox is a dedicated suite of functions, algorithms, and tools designed for various machine learning tasks, including classification, regression, clustering, and dimensionality reduction.

Data Preprocessing Tools: MATLAB provides functions for data preprocessing, allowing users to clean and transform data, handle missing values, normalize features, and encode categorical variables.

Data Visualization Tools: MATLAB's powerful plotting and visualization functions enable users to analyze and visualize data, model predictions, and evaluate metrics. I

Integration with Other MATLAB Toolboxes: Integration with other MATLAB toolboxes, such as the Statistics and Machine Learning Toolbox, enables users to leverage a broader range of statistical and machine learning techniques.

Machine Learning using MATLAB USES: Machine Learning using MATLAB can be applied to a wide range of tasks and applications.

Some common uses of machine learning in MATLAB

Classification: MATLAB can be used for developing classification models to categorize data into different classes. Applications include spam detection, image recognition, and sentiment analysis.

Clustering: MATLAB supports clustering algorithms for grouping similar data points. Clustering can be applied in customer segmentation, anomaly detection, and pattern recognition.

Extraction and Selection: MATLAB helps in extracting relevant features from data or selecting the most important features for building accurate machine learning models.

Deep Learning: MATLAB provides tools for developing and training deep learning models, including neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). This is used in image recognition, speech recognition, and more.

Data Preprocessing Tools: In Machine Learning using MATLAB, data preprocessing is a crucial step to ensure that the data is clean, relevant, and suitable for training machine learning models.

Modules of Machine Learning using MATLAB:

Modules: 1- Introduction to Machine Learning
Modules: 2- MATLAB Fundamentals & Tool Box
Modules: 3- GUI Graphs & Plots in MATLAB
Modules: 4- Graphical User Interface - I
Modules: 5- Graphical User Interface - II in MATLAB
Modules: 6- Commands Control Statements & Loops
Modules: 7- Pre Processing of Data in MATLAB
Modules: 8- Linear Regression in MATLAB
Modules: 9- Logistic Regression in MATLAB
Modules: 10- Decision Tree in MATLAB
Modules: 11- Object Classification using SVM
Modules: 12- Naive Bayes Algorithm in MATLAB
Modules: 13- Find out the nearest neighbor using KNN ( Code )
Modules: 14- K Means Clustering
Modules: 15- Random Forest Algorithm in MATLAB
Modules: 16- Dimensionality Reduction Algorithm in MATLAB
Modules: 17- Regression using Robust Boost
Modules: 18- XG Boost
Modules: 19- RusBoost
Modules: 20- LS Boost
Modules: 21- Imaging Fundamentals & Processing Codes
Modules: 22- De Noising Images
Modules: 23- Filters in Images
Modules: 24- Compression using SWT & DWT
Modules: 25- SVM Algorithm
Modules: 26- KNN Algorithm Presentation
Modules: 27- Image Labelling Techniques
Modules: 28- NN Based Image Classification
Modules: 29- Evaluation of Key Descriptors in Low-Quality Images
Modules: 30- DNA Fragmentation Pattern & Classification