Module 1: Introduction to MATLAB

• MATLAB overview: matrix laboratory for engineering and scientific computing
• MATLAB interface: command window, workspace, editor, and plots
• Matrices and arrays: creating, indexing, and operating on matrices
• Built-in functions: math, trigonometry, and statistical functions

Module 2: Programming in MATLAB

• Scripts and functions: m-file creation and function syntax
• Control flow: if-elseif-else, switch, for, while, and break
• Debugging: breakpoints, step execution, and workspace inspection
• Cell arrays and structures: heterogeneous data containers

Module 3: Data Visualisation

• 2D plots: plot, scatter, bar, histogram, and pie charts
• 3D plots: surf, mesh, contour, and 3D scatter plots
• Plot formatting: titles, labels, legends, and subplots
• Exporting figures: saving plots as PNG, PDF, and EPS

Module 4: Numerical Methods

• Linear algebra: matrix inversion, eigenvalues, and linear systems
• Numerical integration: integral, trapz, and cumtrapz functions
• Differential equations: ODE solvers ode45, ode23, and ode15s
• Optimisation: fminunc, fminsearch, and linprog functions

Module 5: Signal Processing

• Signal generation: sine, square, sawtooth, and chirp signals
• FFT and frequency domain analysis: fft, ifft, and spectrum plots
• Digital filters: FIR and IIR filter design with Signal Processing Toolbox
• Filtering signals: applying designed filters to noisy data

Module 6: Control Systems with MATLAB

• Transfer functions: defining and analysing with tf() and zpk()
• Root locus and Bode plots: stability analysis with control toolbox
• PID controller design: pidtool and closed-loop simulation
• Simulink overview: block diagram modelling and simulation

Module 7: Machine Learning with MATLAB

• Statistics and Machine Learning Toolbox: overview
• Classification: decision trees, SVM, and k-NN classifiers
• Regression: linear, polynomial, and Gaussian process regression
• Deep Learning Toolbox: building and training neural networks in MATLAB

Module 8: Capstone Project and Assessment

• Signal processing project: noise removal and frequency analysis
• Control system design: PID tuning and closed-loop response simulation
• Machine learning classification: training and evaluating a classifier
• Final assessment and course certification

Experience in the Industry Learn from MATLAB-certified engineers who have used MATLAB for signal processing, control system design, numerical simulation, and machine learning in aerospace, automotive, telecommunications, and research organisations.

Backgrounds at the Top Our MATLAB trainers have worked at engineering R&D teams, defence laboratories, and universities where MATLAB is the primary tool for mathematical modelling, simulation, and algorithm development.

Clear & Effective Teaching MATLAB fundamentals, programming, data visualisation, numerical methods, signal processing, control systems, Simulink, and machine learning toolboxes are explained with real engineering and scientific computing examples.

Hands-On Learning Focus Students write MATLAB scripts and functions, plot and analyse data, solve differential equations, design digital filters, build control systems, and train machine learning models through structured hands-on MATLAB labs.

Up-to-Date Knowledge Trainers keep content current with the latest MATLAB and Simulink releases, MATLAB Online, and evolving engineering simulation and data analysis best practices.