MATLAB is the standard computing environment for engineering, signal processing, and control systems. This course covers the matrix-first mental model, plotting and data visualization, DSP fundamentals, Simulink for dynamic systems, and the toolboxes that cover specialized domains.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on matlab and engineering computing you can find — even without producing a single minute of custom video.
This course is built by people who ship production matlab systems for a living. It reflects how things actually work on real projects — not how the documentation describes them.
Every day has working code snippets you can paste into your editor and run right now. The emphasis is on understanding what each line does, not memorizing syntax.
Instead of shooting videos that go stale in six months, Precision AI Academy links to the definitive open-source implementations, official documentation, and the best conference talks on the topic.
Each day is designed to finish in about an hour of focused reading plus hands-on work. You can do the whole course over a week of lunch breaks. No calendar commitment, no live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
The MATLAB workspace, matrix creation and indexing, element-wise vs matrix operations, built-in functions, and the vectorization mindset that replaces explicit loops.
plot, scatter, bar, histogram, subplot, figure formatting, and the figure export workflows used in engineering reports and papers.
The Discrete Fourier Transform, FFT implementation, FIR and IIR filter design, and spectrograms for time-frequency analysis of signals.
Block diagram modeling, solver selection, scope visualization, and simulating feedback control systems and physical models.
Control System Toolbox for Bode plots and pole placement, Statistics Toolbox for regression and hypothesis testing, and Deep Learning Toolbox for training networks.
Instead of shooting our own videos, Precision AI Academy links to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
MATLAB environment, matrix operations, and the vectorization approach to computation.
FFT, filter design, and spectrograms using MATLAB’s Signal Processing Toolbox.
Block diagram modeling, simulation, and analysis of dynamic systems in Simulink.
Transfer functions, Bode plots, root locus, and PID controller design.
The best way to understand any technology is to read the production-grade implementations that prove it works. These repositories implement patterns from every day of this course.
The Python equivalent of MATLAB’s matrix operations. After this course, NumPy is the open-source path to the same numerical computing capabilities.
Python’s signal processing and control systems library. The scipy.signal module directly parallels MATLAB’s Signal Processing Toolbox.
Python plotting comparable to MATLAB’s plot command. The transition path for MATLAB users moving to open-source tools.
Curated list of MATLAB toolboxes, packages, and resources. The reference for extending MATLAB beyond what’s in this course.
MATLAB is required in most engineering programs. This course teaches both the syntax and the engineering intuition that makes MATLAB code produce correct results.
FFT, filter design, and spectrograms are Day 3. This course builds the MATLAB proficiency to apply DSP theory to real signals.
Bode plots, pole placement, and Simulink simulation are Day 5. This course teaches the MATLAB workflow for control system design and analysis.
The 2-day in-person Precision AI Academy bootcamp covers MATLAB and engineering computing hands-on. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
Reserve Your Seat