MATLAB Programming Simulink: Beginner to Pro with ChatGPT

Create and manage MATLAB variables in the command window and scripts, assign and overwrite values, perform arithmetic, handle errors, and inspect data types in the workspace.

Explore Matlab data types—from unsigned and signed integers to single and double precision—along with logicals, characters, strings, cells, and structures, plus typecasting basics.

Explore how to type cast MATLAB variables between double, uint8, and char, using uint8(a) or cast(a, 'double'), and understand overflow and precision when computing means with unsigned integers.

Learn to create 3D and higher-dimensional matrices in MATLAB, populate each layer across the third dimension, replicate to form 4D–6D structures, and visualize them in the workspace.

Learn to create 2d matrices in MATLAB, index and update elements by row and column, extract values, modify rows or columns, and handle dimension errors.

Master matrix indexing in MATLAB to access and update data with a single command, enabling vectorization and faster computation. Explore selecting rows, columns, and submatrices using colon notation.

Explore Matlab matrix operations on a 3 by 4 matrix M using length, size, max, min, sort, and sum, with R and C storing dimensions and the linear index.

Explore built-in MATLAB functions for matrix operations, including identity, ones, and zeros matrices, and generate random or normally distributed values with rand and randn for signal and image processing.

Learn to create row and column vectors in MATLAB using brackets, spaces, and semicolons, and understand how row vectors differ from column vectors in storage and display.

Master the colon operator in MATLAB to extract rows and columns, manipulate matrix values, and generate sequences with adjustable step sizes, from 1 to 10 and beyond.

Learn to perform element-by-element operations in MATLAB, including element-wise addition, multiplication, division, exponentiation, and square-rooting for vectors and matrices using dot notation.

Master logical indexing and linear indexing in MATLAB to select values greater than four, obtain logical index matrices, and use find to retrieve linear indices and corresponding values.

Learn to concatenate two matrices in MATLAB by placing one beside the other or above/below, using row-wise or column-wise approaches, with dimension checks and common errors.

Learn essential MATLAB matrix operations, including addition, subtraction, multiplication, dot product, cross product, and transpose, with attention to matrix dimensions and potential errors.

Export data from MATLAB by saving a matrix as .mat and .csv files, load them back, and organize files with folder paths.

Create a 256 by 256 rgb image in MATLAB by building red, green, and blue channels and casting to uint8, then display it and export as png to a folder.

Construct a MATLAB table with serial numbers, stock names, quantities, and prices, then export it to txt and csv formats. Learn to specify file names and paths for saving tables.

Learn to import data from a text file into MATLAB, handling spaces or commas as delimiters, managing header lines, and extracting data into a clean matrix for analysis.

Learn to import data into MATLAB from csv and text files, converting between tables, matrices, and cells using read table, read matrix, read cell, and read text matrix.

Learn to import images and videos into Matlab, loading them into a 3d rgb matrix. Display color channels with imshow and navigate file paths and folders to avoid errors.

Learn how to load an mp4 video into Matlab, create a video reader object, and loop through frames to display and process rgb24 frames for video processing.

Construct a 3D surface plot in MATLAB by creating a mesh grid, defining x, y, and z values, and customizing the plot properties.

Learn to create discrete plots in MATLAB by converting continuous plots with sampling rate, using stem, and customizing color, width, style, and markers for both discrete and continuous visuals.

Enhance MATLAB plots by adjusting line styles, colors, markers, fonts, and legends, optimize legend positioning and multi-column layout, and export high-resolution figures suitable for research papers.

Export MATLAB plots with 600 dpi via export setup to improve resolution and remove white space, then adjust marker size and fonts (including LaTeX) for publication-ready figures.

Import data from a csv into MATLAB, convert to a matrix, split into four vectors, and plot them with labels, a title, and legend for SVM, KKNN, GRU, and LSTM.

Master plotting in MATLAB by creating x and y data, plotting sine functions, labeling axes, and adding titles, with techniques for combining multiple plots into a single figure.

Learn to create plots in matlab, generate x and y vectors, visualize a sine function with axis labels and a title for a single graph.

Create multiple plots in a single figure using subplot, arranging them in 1x2, 2x1, or 2x2 grids; label axes, and set titles for the angle versus sine and cos graphs.

Discover how if-else-end in MATLAB implements conditional logic by executing the if block when true and the else block when false. See age and value examples determine the displayed messages.

Learn to implement the if-elseif-else-end structure in MATLAB, handling true and false branches, nesting conditions, and creating interactive prompts like age eligibility and value checks.

Learn how to use if-end conditional statements in MATLAB, building boolean expressions, processing user input, and displaying messages to simplify decision making.

Explore the switch-case conditional statement in MATLAB, showing how to map grades to messages using cases, a default otherwise, and an end. Practice with grade input.

Explore how to use for loops in MATLAB to iterate, update vectors x and y, and plot results, while understanding loop syntax, indexing rules, and common errors.

Master nested for loops in MATLAB to build matrices, iterate over rows and columns, and record the locations where matrix values exceed 20.

Explore the while loop in MATLAB with practical examples that increment x by five until 100, store results in y, index with i, and visualize the outcomes.

Create a no-output function to automate plotting in MATLAB, comparing built-in and user-defined functions and accepting inputs for x, y, color, marker, and line width.

Create a MATLAB function with inputs and outputs, save it in the current folder, and call dist with x1, x2, y1, y2 to compute the distance between two points.

Learn how to create user defined functions in MATLAB, using function syntax, input and output arguments, and main script calls to perform matrix operations like sum, product, and inverses.

Learn how to create and call user-defined functions in MATLAB, use input and output arguments, and streamline your code by calling a single function from the main script.

Learn to build interactive MATLAB apps with App Designer, using a library of UI components and two views—designer and code—to design, drag-and-drop features, and run and save apps.

Build and customize 2D plotting GUI in MATLAB App Designer by configuring axis properties, labels, fonts, and ticks, then connect a push button callback to plot data on axis one.

Design a calculator GUI in Matlab app designer, arrange a five-by-four button grid with labels and an edit display, then save, run, and wire button callbacks.

Create callbacks for the 0–9 and decimal point buttons in matlab app designer to load num1 and num2, update the lcd, concatenate input, and implement reset.

Learn to implement operator buttons in MATLAB app designer, manage an operator flag and two operands, convert strings to numbers, and display results on the lcd.

Explore debugging strategies for a MATLAB app calculator built with App Designer, identifying and fixing errors in operations, reset behavior, and result handling.

Package the calculator by Fawad as an add-on in MATLAB app designer, set package name and icon, select the main file, and auto-load dependencies for installation from the network library.

Convert a MATLAB app into a standalone desktop program by installing the application compiler, packaging the project, and distributing the executable for PC users.

Explore MATLAB's machine learning toolbox, focusing on the classification learner app, supervised and unsupervised methods, training with cross-validation, and interpreting confusion matrices for accuracy and overfit.

Identify data types, normalize data to a standard range, and split it into 80% training and 20% testing sets for model learning and validation in Matlab.

Generate random data, train a linear regression model with fitlm, and assess performance by predicting unseen data and comparing results to true values.

Load and explore the fisheries dataset in Matlab, convert labels to categorical, and visualize with 2D plots to prepare for building a k-nearest neighbor AI classifier.

Train a k nearest neighbor classifier on the train set, predict the test set, and evaluate accuracy using a 30% holdout cross-validation split.

Explore the knn classifier in Matlab, evaluating performance with accuracy and misclassified samples. Visualize results using a confusion matrix that shows true vs predicted labels.

Demonstrate training and testing an ensemble classifier and evaluating its performance with a confusion matrix, comparing to a KNN baseline; bagging performs best among tested methods.