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MATLAB
MathWorks MATLAB — Engineering Computation & Simulation

Compute, Simulate

& Visualise

From matrix mathematics and signal processing to Simulink system modelling and deep learning — master MATLAB, the world's leading technical computing platform used by engineers, scientists, and researchers in aerospace, automotive, electronics, and biomedical fields.

Matrix Operations Signal Processing Simulink Control Systems Machine Learning Image Processing Data Analytics FFT Analysis PID Tuning Deep Learning Toolbox
Enroll Now Brochure
MATLAB R2024b — Signal Analysis & Simulink
MATLAB R2024b HOMEPLOTSAPPSEDITORSIMULINK Current Folder signal_analysis.mcontrol_pid.mimage_proc.mml_classifier.msimulink_model.slxdata_load.matresults.xlsxreport.pdf Workspace t1×1000 doublesignal1×1000 doublefreqdoublefft_datacomplex doubleFs8000f1×501 doublemagnitude1×501 double signal_analysis.m 1%% Signal Processing Analysis2Fs = 8000; % Sampling frequency3t = 0:1/Fs:1-1/Fs;45% Generate composite signal6f1 = 440; f2 = 1200;7signal = sin(2*pi*f1*t) + ...8 0.5*sin(2*pi*f2*t);910% FFT Analysis11N = length(signal);12fft_data = fft(signal);13magnitude = abs(fft_data(1:N/2+1));14f = Fs*(0:N/2)/N;1516% Plot results17figure; subplot(2,1,1);18plot(t(1:200), signal(1:200));19xlabel('Time (s)'); ylabel('Amplitude'); Command Window >> signal_analysis Sampling rate: 8000 Hz Signal duration: 1.0 s Peak frequency: 440.0 Hz (f1 confirmed) SNR: 24.6 dB >> Figure 1: Signal & FFT Time Domain Signal 00.025 s0 FFT Magnitude Spectrum 440Hz1A1200Hz0.5AFrequency (Hz) Simulink: PID Control #ea580c#3b82f6#16a34aFeedback Loop Ready  |  Ln 17, Col 28  |  signal_analysis.m  |  MATLAB R2024b
12
Modules
Engineering computation
65+ hrs
Duration
Hands-on
Cert
Included
Industry recognised
24/7
Support
Expert guidance
What You Master

The Complete MATLAB & Simulink Skill Set

From matrix operations, signal processing, and control systems to deep learning, image processing, and Simulink model-based design.

🧮
Complete Engineering Computation & Simulation Platform

Master MATLAB's complete engineering workflow — matrix and linear algebra, signal processing with FFT and filtering, control system design with root locus and Bode plots, Simulink block-diagram modelling for dynamic systems, machine learning classification and regression, and deep learning with neural networks for real-world engineering applications.

Signal ProcessingSimulinkControl SystemsMachine LearningDeep Learning
📊
Signal Processing & FFT

Digital filtering (FIR/IIR), spectral analysis with FFT, windowing functions, and noise reduction for audio and sensor data.

⚙️
Control Systems

Transfer functions, state-space, PID tuning, root locus, Bode/Nyquist plots, and system stability analysis with the Control Toolbox.

12
comprehensive modules from MATLAB scripting to Simulink, machine learning, and deep learning toolboxes.
🔷
Simulink Modelling

Block-diagram simulation of dynamic systems: mechanical, electrical, hydraulic, and hybrid system models.

🧠
Machine & Deep Learning

Classification, regression, clustering, CNNs for image recognition, and LSTM networks using Statistics and Deep Learning Toolboxes.

CNN Training
net = alexnet;
layers = net.Layers;
layers(end-2) = ...
fullyConnectedLayer(5);
Accuracy: 94.2%
🖼️
Image Processing

Filtering, morphological operations, edge detection, segmentation, and feature extraction with the Image Processing Toolbox.

📈
Data Analytics

Statistical analysis, regression, curve fitting, hypothesis testing, and interactive data visualisation with 2D/3D plots.

Industry Applications
Aerospace · Automotive · Electronics · Biomedical · Finance

MATLAB is used by NASA, ISRO, Bosch, and every major research institution. It is the standard computation environment for control systems, signal processing, and simulation-based design across all engineering domains.

Why MATLAB

The Universal Language of Engineering Analysis

MATLAB is used by over 6 million engineers and scientists worldwide and is the standard tool for research, algorithm development, and system simulation across all engineering disciplines.

Matrix & Numerical Computing

MATLAB's core is built for matrix operations, linear algebra, and numerical methods — solve differential equations and eigenvalue problems faster than any other environment.

Signal & Image Processing

Built-in FFT, filter design, wavelet transforms, and image processing functions make MATLAB the preferred tool for DSP engineers globally.

Simulink: Industry-Standard Simulation

Simulink is used by every major automotive OEM (Bosch, Continental, TATA) for model-based design and embedded code generation (C/C++ via Embedded Coder).

AI & Machine Learning

MATLAB's Statistics, Machine Learning, and Deep Learning Toolboxes bring AI/ML capabilities directly into the engineering workflow without context-switching.

Required in Core Engineering Roles

Signal processing, control systems, and research engineering roles at ISRO, DRDO, TCS, and Infosys require MATLAB as a prerequisite.

Tools & Technologies You'll Master
MATLAB R2024b
Simulink
Signal Processing TB
Control System TB
Statistics & ML TB
Deep Learning TB
Image Processing TB
Optimization TB
Parallel Computing
Embedded Coder
Stateflow
Simscape
Data Acquisition TB
Curve Fitting TB
MATLAB Online
MATLAB R2024b
Simulink
Signal Processing TB
Control System TB
Statistics & ML TB
Deep Learning TB
Image Processing TB
Optimization TB
Parallel Computing
Embedded Coder
Stateflow
Simscape
Data Acquisition TB
Curve Fitting TB
MATLAB Online
Curriculum

12-Module MATLAB Mastery Programme

From scripting and matrix algebra to signal processing, Simulink, control systems, and deep learning.

  • MATLAB workspace, editor, and command window
  • Variables, data types, and arrays
  • Matrices: creation, indexing, slicing, operations
  • Control flow: for, while, if-else, switch
  • Functions: anonymous, named, nargin/nargout
  • Matrix arithmetic: addition, multiplication, transpose
  • System of linear equations: backslash operator
  • Eigenvalues and eigenvectors
  • Singular Value Decomposition (SVD)
  • Sparse matrices and memory efficiency
  • 2D plots: plot, scatter, bar, histogram, polar
  • Multiple subplots and figure formatting
  • 3D plots: surf, mesh, contour, patch
  • Animation and dynamic updating plots
  • Export: PNG, PDF, EPS for publication-quality figures
  • Descriptive statistics: mean, std, var, percentile
  • Regression: linear, polynomial, curve fitting
  • Hypothesis testing: t-test, ANOVA, chi-square
  • Correlation and principal component analysis (PCA)
  • Loading and processing Excel, CSV, and MAT data files
  • Sampling, aliasing, Nyquist theorem
  • Discrete Fourier Transform (DFT) and FFT
  • FIR and IIR filter design: Butterworth, Chebyshev
  • Spectral analysis: power spectral density, spectrogram
  • Wavelet transforms for non-stationary signals
  • Transfer functions and state-space representation
  • Step response, impulse response, frequency response
  • Root locus analysis and stability criteria
  • Bode plots and Nyquist diagram
  • PID controller design and tuning (pidTuner app)
  • Simulink environment: blocks, ports, signal lines
  • Continuous vs. discrete simulation
  • Source blocks, sink blocks, and math blocks
  • Scope and display for simulation results
  • Subsystem creation and masking for model organisation
  • S-Functions for custom block development
  • Fixed-step and variable-step solvers
  • Stateflow: finite state machines and flow charts
  • Embedded Coder: C code generation from Simulink
  • Hardware-in-Loop (HIL) testing concepts
  • Image read/write, display, and colorspace conversion
  • Spatial filtering: Gaussian blur, sharpening
  • Edge detection: Sobel, Canny, Laplacian
  • Morphological operations: erosion, dilation, opening
  • Image segmentation and feature measurement
  • Supervised learning: kNN, SVM, decision tree
  • Unsupervised: k-means, hierarchical clustering
  • Feature engineering and cross-validation
  • Classification Learner App for model comparison
  • Regression: linear, SVM regression, ensemble methods
  • Neural network architecture: layers, activations
  • Convolutional Neural Networks (CNN) for image classification
  • Training loop: loss functions, optimisers, learning rate
  • Transfer learning with AlexNet, VGG, ResNet
  • LSTM and GRU networks for time-series data
  • Parallel computing: parfor, GPU arrays
  • Large dataset processing with tall arrays
  • MATLAB App Designer: building custom GUIs
  • Live Scripts for interactive technical reports
  • Capstone project: end-to-end engineering simulation pipeline
Course Snapshot
12 Modules
Scripting to Deep Learning
65+ Hours
Total learning
Certificate
On completion
Tech Support
Call / WhatsApp
Mon–Fri
9 AM – 6 PM
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Pricing & Packages

Get a Custom Quotation

Flexible pricing for video, live, and blended modes — reply within 24 hours.

Career Outcomes

MATLAB Careers Across Engineering & Research

MATLAB skills are required in aerospace, defence, automotive, electronics, and research organisations across India and globally.

ISRO
Space Research
DRDO
Defence R&D
Bosch India
Automotive
Texas Instruments
Semiconductors
Signal Processing Engineer
Design and implement digital filters, FFT algorithms, and spectral analysis systems for audio, communication, and sensor applications.
₹5–14 LPA
Control Systems Engineer
Design PID, LQR, and model-predictive controllers for robots, CNC machines, HVAC, and automotive ECU development.
₹6–16 LPA
Research & Simulation Engineer
Conduct numerical simulations, finite element analysis prep, and algorithm development using MATLAB in academic or industrial R&D.
₹5–12 LPA
Machine Learning Engineer
Build and deploy ML models using MATLAB's toolboxes for predictive maintenance, fault detection, and process optimisation.
₹7–18 LPA
Embedded Systems Designer (Simulink)
Use Simulink and Embedded Coder to generate production C/C++ code for automotive ECUs, aerospace flight computers, and industrial controllers.
₹6–15 LPA
12
Modules
65+
Hours of Training
₹5–18L
Salary Range
6M+
Global Users
New Batch Starting Soon — Limited Seats

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